Low-Cost Imaging Photometer and CalibrationMethod for Road Tunnel Lighting

Stefano Cattini, Member, IEEE, and Luigi Rovati, Member, IEEE

Abstract—A camera-based measuring instrument for road tun-nels lighting is proposed. The system is aimed at estimating theveiling luminance as it will be perceived by a driver approachingthe tunnel, thus allowing the estimation of the optimum luminancelevel of tunnel entrances, hence increasing the driver’s safety.The proposed measuring instrument and the relative calibrationmethod are based on a low-cost commercial grade camera and areference standard, respectively.

Index Terms—Calibration, image sensors, lighting control,lighting, optical variables measurement, photometry, road trans-portation.

I. INTRODUCTION

IN this paper, which is an extended version of the paperpresented at I2MTC 2011 [1], we report our activities aimed

at developing and calibrating a veiling luminance measuringsystem for tunnel-lighting applications. A more detailed analy-sis of both the proposed measurement procedure and calibrationmethod is here proposed. Moreover, further considerationsabout the target measurement uncertainty of the veiling lumi-nance estimation have been proposed. On the contrary, for adetailed description of both the measurement principles andthe normative context, the reader can refer to the conferencepaper [1].

“The aim of tunnel lighting is to ensure that users, both dur-ing the day and by night, can approach, pass through, and exitthe tunnel without changing direction or speed with the degreeof safety commensurate to that on the approach road” [2].

In tunnels lighting, the threshold zone lighting—the lightingof the first part of the tunnel, directly after the portal—playsa key role in traffic safety being the lighting of the thresholdzone of the tunnel which allows drivers to see into the tunnelwhile in the access zone, thus avoiding drivers experiencingthe hazardous “black hole” effect. Therefore, during the day,luminance levels in the threshold and transition zones need tobe greater or equal than a constant percentages of the accesszone luminance.

Appropriate construction strategies such as louvres designedto exclude sunlight and low-reflectance road surface materials

Manuscript received June 16, 2011; revised November 27, 2011; acceptedNovember 28, 2011. Date of publication February 10, 2012; date of currentversion April 6, 2012. The Associate Editor coordinating the review processfor this paper was Dr. Zheng Liu.

The authors are with the Department of Information Engineering, Uni-versity of Modena and Reggio Emilia, 41125 Modena, Italy (e-mail:stefano.cattini@unimore.it).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TIM.2011.2180966

may allow to reduce the luminance of the surroundings of thetunnel [3], [4]. Nevertheless, changes in day-light conditions ofthe external luminance level result in variations of the minimumrequired access zone luminance, whereas higher access zoneluminance level will results in energy wasting and higher costsand may lead to the driver dazzle, too. Therefore, it is advisableto provide automatic control of the artificial lighting in thesezones.

Standards and recommendations of European Commission,PIARC and other professional bodies of the European Uniondefine minimal technological requirements for equipment andoperation of the tunnels in scope of Trans-European RoadNetwork. Italian tunnel lighting is regulated by the UNI 11095national standard [5]. In accordance with CIE 88-1990 [6], day-time lighting requirements of long tunnels may be determinedfrom the estimation of the veiling luminance Lv .

In this paper, we propose the use of camera system connectedto a personal computer (PC) in order to estimate the veiling lu-minance. The developed measuring system has been calibratedby using a luminance standard realized exploiting a reflectancestandard illuminated by a xenon lamp.

After a brief review of both the tunnel-lighting normativecontext and theoretical background (Section II), some consid-erations about the target measurement uncertainty are reported(Section III).

Both the developed measuring system and the proposedmeasurement method are presented in Section IV. Then, theproposed calibration method and the relative calibration curves,and diagrams are reported in Section V. Conclusions are drawnin Section VI.

II. THEORETICAL BACKGROUND

Luminance is the photometric equivalent of radiance and isdefined as the luminous flux, at an angle θs to the normal of thesurface element, to the infinitesimal elements of area As perunit solid angle Ω [7]

L =dΦv

cos θsdAsdΩ(1)

where Φv is the luminous flux (photopic vision).Depending on the level of light adaptation, the luminous flux

can be estimated according to CIE standards as [7]

Φv = Km

∫Φλ(λ)V (λ) dλ

Φ′v = K ′

m

∫Φλ(λ)V ′(λ) dλ (2)

where Φλ is the radiant power per wavelength interval, Km andK ′

m are the luminous efficacies and, V (λ) and V ′(λ) are thespectral efficiency functions, for photopic and scotopic vision,respectively.

Although human visual system can operate over about12 log units of luminance in total, it can only operate over aboutthree log units simultaneously [8]; thus, when it is adapted toa given luminance, much higher luminances appear as glar-ingly bright, while much lower luminances are seen as blackshadows [8].

As previously stated, Italian tunnel lighting is regulated bythe UNI 11095 national standard [5]. From UNI 11095, therequired luminance levels in the entrance zone of the tunnelmay be determined from the estimation of the veiling luminanceLv [5]

Lv = Lseq + Latm + Lwinds (3)

where Lseq is the equivalent veiling luminance, Latm is the at-mospheric luminance and Lwinds is the windscreen luminance.As discussed in the conference paper [1], the key quantity is theequivalent veiling luminance Lseq .

Among the eight different glare forms classified by Vos [9],the equivalent veiling luminance refers to the disability glare,and it is defined by comparing the visibility of an object seen inthe presence of the glare source, with the visibility of the sameobject seen through a uniform luminous veil [8]. The equivalentveiling luminance of the glare source is then defined as theluminance of the veil that provides the same visibilities as theglare source.

From the beginning of the 20th century, many empiricalmethods for estimating the equivalent veiling luminance havebeen proposed [10]–[12], and also CIE standards are available[13], [14]. According to CIE standards [13], [14], the UNI11095 standard allows to estimate the Lseq value by two meth-ods: (i) estimating the illuminance at the eye from any of theglare sources (lx) as it would be seen by a driver approachingthe tunnel (observer oriented) or, (ii) estimating the luminance(cd/m2) of the objects viewed by a driver approaching thetunnel (object oriented).

As it will be described in Section V, the proposed calibrationmethod is based on a measurement standard instead of usingan expensive reference calibrated system, thus the “object-oriented” definition is more convenient. As a result, accordingto UNI 11095 standard, the Lseq has been measured by usingthe polar diagram shown in Fig. 1 which is consistent with thestudies from Adrian [15]. Then, observing the tunnel from anheight to the ground of 1.5 m, a distance to the tunnel equalto the stopping distance SD [5] and making the center of thepolar diagram overlap with the tunnel portal, the Lseq value isdefined as [5]

Lseq = k1 ·9∑

i=1

12∑j=1

Lij (4)

where k1 is a constant, i and j are the indexes of rings andsectors, respectively, and Lij is the mean luminance emitted bythe jth sector of the ith ring.

Fig. 1. Sketch of a practical application scene. As it can be seen, the scene isgenerally composed by different objects, thus giving rise to luminances spreadover several orders of magnitude. The polar grid (red in color version) is similarto the one provided by UNI 11095 standard for the Lseq estimation process.Such as the UNI 11095 standard polar diagram, it is composed of nine ringseach one divided in 12 sectors; the inner and outer rings subtending an angleof about 1◦ and 28◦, respectively, with the apex at the position of the eye ofan approaching driver looking at the center of the tunnel entrance and whosedistance from the tunnel is equal to the stopping distance SD. The inner circle(±1◦, fovea) is excluded from summation in (4). The diagram is pruned inorder to take into account the stop effect provided by the vehicle windscreen.

As discussed in the conference paper [1], even consideringa cosine corrected system—a system responding equally toradiation at any point across its surface and from any angleθ—the Lseq may not be directly estimated using a simplephotodiode. Therefore, even though systems based on a pho-todiode and motorized optics have been proposed, i.e., [16],the developed veiling luminance measurement method is basedon a camera system. Hence, in the following, three referencesystems are used: the “world coordinates” (x, y, z) relativeto the objects, the “camera coordinates” (x′, y′, z′) relative tothe image and, the “pixel coordinates” (r, c)—r and c are therow and column indexes, respectively—relative to the signalprovided by photosensor array. In such coordinate systems,both the x and x′ axes are aligned with the optical axis of thecamera system and x′ = 0 at the image plane. Then, interceptof the optical axis with the CCD plane is the origin of the pixelcoordinates.

An optical system can be properly approximated as a linearshift invariant system. Therefore, it can be described by apoint spread function, and the illuminance on the image planeis given by convolving the object luminance projected ontothe image plane with the point spread function [17]. Underfar field condition (d � f , d′ � f ; being f , d, and d′ thefocal length of the optical system, the distance of the objectfrom the objective front focal point and the distance of theimage from the back focal point, respectively), the illuminanceE ′(y′, z′) on the image plane of an imaging system developedby a Lambertian radiator having luminance L(x, y, z) can beapproximated as [17]

E ′(y′, z′) = T · π[L(x, y, z)(f/#)2

]· cos4 θ (5)

where T is the optical system transmittance, f/# is thef -number of the optical system and θ is the angle of inci-dent light to the system optical axis. Hence, the object lumi-nance could be easily estimated from the image illuminance

regardless to the actual distance of the object from the objectivefront focal point d.

On the other hand, the illuminance E′(y′, z′) at the imageplane is revealed by a suitable sensor (usually CCD) andconverted into an electrical signal at discrete positions. There-fore, considering a monochrome camera and using the pixelcoordinates (c, r), the relation between E′(y′, z′) collected bythe considered photosensor (r, c), and the provided grey levelg(c, r)—the digital number—is [17]

g(c, r) = t

∫Δy′

∫Δz′

λ2∫λ1

Rv(λ) · E ′λ(y′, z′) dλdz′dy′ (6)

where, Δy′ and Δz′ are the pixel horizontal and vertical dimen-sions, respectively, [λ1, λ2] is wavelength range of the imageilluminance, E′

λ is the image spectral illuminance, Rv(λ) isthe digital sensor (photometric) spectral responsivity relativeto the (r, c) pixel and t is the exposure time. (6) has beenobtained neglecting any noise and supposing that the imageilluminance E′(y′, z′, λ) (the object luminance L(x, y, z, λ))did not significantly change during the exposure time t.

Concluding, from (5) and (6), g(c, r) is proportional toE′(y′, z′), thus to L(x, y, z).

III. CONSIDERATIONS ABOUT THE TARGET

MEASUREMENT UNCERTAINTY

There is probably a wide variety of measurement and cal-ibration methods pair able to provide an estimation of theequivalent veiling luminance Lseq . Over the years, severalmeasurement methods have been proposed, i.e., [15], [18]–[20],and accurate measurements may be probably performed us-ing a measurement method similar to the one proposed byMefford et al. [21]. However, for any practical application,the more suitable measurement and calibration methods pair isprobably the simpler and lower costing one among the pairsable to provide a measurement uncertainty lower or equal tothe target measurement uncertainty.

Unfortunately, UNI 11095 standard does not define themaximum admissible measurement uncertainty. Nevertheless,the target measurement uncertainty required to the measuringsystem is probably “high.” As a matter of fact, as previouslydiscussed, human visual system can operate over about threelog units simultaneously [8] and the Lseq estimation may alsobe based on tabulated data, thus allowing high-uncertaintyestimation. Moreover, in a wide range of luminance values,humans cannot resolve relative luminance differences lowerthan 2% [17].

In order to assess the target uncertainty, it is important toconsider the “scope of the measurement.”

The developed measuring system is aimed at estimating theveiling luminance as it will be perceived by an unknown driverapproaching the tunnel entrance. Luminous flux of (2) is themeasurement model used for estimating the visual sensationfrom the measured radiometric quantities. Therefore, any intra-and interindividuals deviation from the Km and V (λ) is an

Fig. 2. Luminous efficacy and spectral efficiency function for photopic andscotopic vision, respectively. Data have been derived from Zalewski [7].

unavoidable component of definitional uncertainty (intrinsicuncertainty).

Photometry is aimed at measuring visible optical radiation insuch a way that the results correlate with the visual sensationof a “normal human observer” exposed to that radiation [22];however, there are obviously large individual differences [23]–[25]. Even though, an accurate discussion about the limitationsof the spectral luminous efficiency for photopic vision V (λ)function [26] is beyond the scope of the current paper an may befound elsewhere [23]–[25], in addition to interindividual differ-ences, “the shape of the luminous efficiency function changeswith chromatic adaptation, thus, any luminosity function isonly of limited applicability, since it is not generalizable toother conditions of chromatic adaptation, or necessarily to othermeasurement tasks” [23]. As an example, Fig. 2 shows theKm · V (λ) and K ′

m · V ′(λ) functions for photopic and scotopicvision; as stated by Zalewski [7]: “Because of the complexityof the spectral sensitivity of the eye at intermediate irradiationlevels, there is no [yet] standard of spectral efficiency formesopic vision.”

Moreover, it is known that V (λ) underestimates the pho-toreceptor responsivity in the UV region. As stated byStockman et al. [27]: “. . .the 1924 V (λ) function deviates fromtypical luminosity data (e.g., filled squares, open circles) by afactor of nearly ten in the violet, a problem that continues toplague both colorimetry and photometry 75 years later.” As aresult, as reported by the CIE 041 [28], the brightness of anobject lighted by a mercury lamp may appear twice than thatgenerated by a sodium lamp, for equal luminance.

The limitation of the CIE V (λ) function is obviously notnew, but as discussed by Wyszecki and Stiles in 1982 [29]:“any minor improvement at this stage would be outweighed bythe very considerable practical inconvenience of a change in thebasic function on which all photopic photometry has been basedfor more than 50 years.” Therefore, accurate measurementsmay be required when the aim is to analyze the behavior ofwell-defined individual/s, but may result unnecessary if theaim is to obtain an estimation of veiling luminance as it willbe perceived by an unknown driver approaching the tunnelentrance.

As a matter of fact, even though in 2002, the CIE hasproposed an improved equation for the veiling luminance es-timation [13] that takes into account the age of the observer, theUNI 11095 standard obviously did not take into account suchunknown quantity of influence.

Finally, in addition to interindividual variability, also intercarvariability may affect the veiling luminance estimation. Asexperienced in sunny days and discussed by Mefford et al. [21],windscreen geometry, dashboard reflectance, and interior andexterior car cleaning may highly affect the perceived veilingluminance.

From the above discussion, it may be thought that themeasurement of the equivalent veiling luminance may bringalmost the same results as the usage of tabulated data. Such isobviously false. For instance, the sun at the zenith may giverise to a luminance of about 1.6 · 105 cd/cm2 that reduces to6 · 102 cd/cm2 once it reaches the horizon or even4 · 10−3 cd/cm2 in dark cloudy days [30].

IV. MEASURING SYSTEM AND MEASUREMENT METHOD

The polar diagram of Fig. 1 and the calculation methodreported in (4) are intended to provide an estimation of theaverage luminance contained in a conical field of view (f.o.v.)analogous to the one of an approaching driver, thus the opticsof the measuring instrument has to be designed in order toprovide a f.o.v. as close as possible to one shown in Fig. 1.Neglecting aberrations, from (5) and (6), it is straightforwardto observe that a biunique relation exists between the objectluminance L(x, y, z), the image illuminance E′(y′, z′), andthe grey level g(c, r) provided by a monochrome camera.Hence, the veiling luminance Lseq may be easily estimated byanalyzing the image/s provided by a calibrated camera system.It is important to notice that the Lseq estimation described by(4) is determined by the mean object-sector luminance Lij ,thus resulting quite insensitive to blurring, geometric distor-tion, and optical aberrations. Therefore, also low-cost opticalcomponents could be exploited for the development of veilingluminance measuring instruments. As a result, the prototype ofthe proposed measuring system has been build up by simplyusing a commercial grade camera system (CS) connected to aPC by means of the FireWire port, thus images acquired by theCS were analyzed according to (4) and Fig. 1.

A. Camera System

Measurements have been performed exploiting acommercial-grade monochrome camera (model DMK21AF04, The Imaging Source, Germany). The DMK 21AF04camera is based on a 1/4′′ CCD solid-state image sensor(model ICX098BL, Sony, Japan) with a square pixel array(CCD diagonal dimension equal 4.5 mm), thus a f = 4 mmfocal length objective whit iris (T 0412 FICS-3, The ImagingSource, Germany) has been chosen in order to obtain a f.o.v.analogous to the one shown in Fig. 1. A picture of the camerasystem is shown in Fig. 3.

In order to reduce the system sensitivity to the NIR (nearinfrared) spectral components, a UV-IR-CUT optical filter

Fig. 3. Used camera system. It consists in the DMK 21AF04 FireWiremonochrome camera and T 0412 FICS-3 objective. As shown, a plastic ringhas been added in order to fix the objective settings. Such plastic holder allowsfixing the UV-IR-CUT optical filter also.

(model 486, The Imaging Source, Germany) has been placedin front of the T 0412 FICS-3 objective, thus giving rise to theoverall camera system CS.

Fig. 4 shows the (nominal) normalized spectral responsivityof the ICX098BL CCD (Rλ−ICX) and normalized spectraltransmissivity of the 486 filter (T486).

Even though the Lseq estimation process is quite insensitiveto blurring, if the depth of field includes infinite distance, theminimum object distance dmin for an aberration-free opticalsystem could be roughly estimated as [17]

dmin ≈ f2

2 · f/# · ε =(4 · 10−3m)2

2 · 1.2 · 5.6 · 10−6 m≈ 1.2 m (7)

where ε is the CCD square-pixel lateral dimension [in (7) themaximum iris aperture of the used T 0412 FICS-3 objectivehas been considered]. Hence, being the stopping distance SD ofthe order of tens of meters, the developed CS is reasonably ableto properly image the tunnel scene.

B. Camera Settings

As discussed in the conference paper [1], the image illu-minance E′(y′, z′) is influenced by the lens objective settingseven under the far-field condition (d � f, d′ � f) of (5). Asa result, before starting the calibration activities, the objectivefocus and iris aperture have to be properly set.

Since CS has to be placed at a distance to the tunnel equalto the stopping distance SD, the focus has been set in order toobtain a working distance analogous to the stopping distanceSD (about 120 m) (depth of field at least from SD to infinity).Then, the continuously variable iris aperture has been set inorder to avoid the CCD photosensor saturation. The used DMK21AF04 camera allows nominal exposure time t ranging from2−13 s to 25 s. Therefore, in order to choose the proper irisaperture, the exposure time was set to 2−12 s, and the irisaperture has been empirically set as maximum aperture thatavoids both grey level saturation and blooming once the systemwas exposed to direct summer sunlight. As shown in Fig. 3, aplastic ring has been added in order to both prevent accidentalchanges in the objective settings and reduce the stray-lighteffects.

Moreover, for the dark part of the image, the relative lu-minance resolution of a CCD system is usually lower thanthat of human eyes, thus video cameras generally convert theimpinging illumination E′ not linearly, but with some nonlinear

Fig. 4. Normalized (radiometric) spectral responsivity of the ICX098BL CCD (Rλ−ICX) and, normalized spectral transmissivity of the 486 filter (T486). Itcan be noted that the shape of the expected CS spectral responsivity Rλ−ICX · T486 does not perfectly match the spectral efficiency function V (λ) particularlyin the UV region of the spectra. Better matching may be probably achieved by using the green signal provided by an RGB camera. As an example, the Rλ−green

curve shows the (radiometric) spectral responsivity of the green pixels of the RGB CCD model ICX098BQ (Sony, Japan). (Data have been taken from the CCDsand filter datasheets).

functions such as the Gamma correction or the Logarithmictransform [17]. Even though grey levels lower than 100 areseldom encountered (see Section IV-C), such nonlinear functionroutines have obviously to be disabled in order to properlyestimate the Lseq .

Similarly, also automatic shutter, gain, and offset controlshave to be disabled.

C. Measurement Method

A detailed analysis of the image processing is beyond thescope of the current paper. Nevertheless, it is easy to notice thatin order to reduce the effects due to the CCD signal quantization(grey levels) and increase the signal-to-noise ratio, the use ofthe higher exposure time t able to avoid photosensor saturationis convenient.

However, as schematically shown in Fig. 1, in practicalapplications, different objects (luminances) are imaged at thesame time, thus, in general, there is not a unique optimumexposure time topt, but different pixels may have differentoptimum exposure times.

UNI 11095 standard does not define the minimum frequencyrate at which the Lseq has to be estimated; however, it could besupposed that the luminances of the observed objects changewith frequencies not higher than few tenths of Hertz. There-fore, any Lseq estimation can be reasonably performed usingdifferent exposure times t and acquiring one or more imagesfor each one of them—multi-image capture method. Then, theluminance relative to the object whose image forms on the (c, r)pixel can be estimated by using the image/s acquired with thehighest t able to avoid the (c, r) pixel saturation. Therefore,considering that the used 8-bit greyscale CCD allows nominally

changing t as a power of 2, grey levels lower than 100 areseldom used for the luminance estimation [see (6)].

The usage of different exposure times for different pixelsmay give rise to blooming from the neighboring pixels. How-ever, such problem can be easily detected and prevented bychecking neighboring pixel saturation.

Finally, the luminance map obtained using different exposuretimes t (the best exposure time for any pixel, topt), can beanalyzed by using (4) in order to obtain an estimation of Lseq .

V. SYSTEM CALIBRATION

Calibration of imaging systems requires a luminancestandard—homogeneous calibrated light source—or a refer-ence calibrated imaging system; our calibration method makesuse of a measurement standard.

A luminance standard is traditionally established using a dif-fuse reflectance or transmittance standard—opal glass, integrat-ing sphere or reflectance standard—and a luminous intensitystandard lamp.

Opal glass, illuminated by a known level of illuminance, hasbeen the most widely used luminance standard because of itslow cost, long-term stability, and ease of handling [31]. Despitethe simplicity of such device, accurate measurements with anopal glass are not trivial since it is sensitive to stray light fromboth sides of the glass, and extreme care should be taken tominimize ambient reflections from the front and the back sides[31]. Moreover, an opal glass should be carefully uniformlyilluminated over its entire surface area [31].

On the other hand, integrating spheres may establish lumi-nance standards with less uncertainty and difficulty than thetraditional methods using a diffuse reflectance or transmittance

standard [31]. As a matter of the fact, imaging sensors areusually calibrated using integrated spheres [17], [32].

A CCD-system photometric calibration for road-lighting ap-plication has been already proposed by Fiorentin et al. [33].Such calibration method was based on a “three-step” procedure:(i) removing the objective lens from the camera system andimaging the luminance developed by a closer placed integratingsphere—the sphere was positioned close enough to allow theimage of its output luminance to cover the entire sensing areaof the CCD; (ii) adding the objective lens to the camera systemand imaging the luminance developed by the closer placedintegrating sphere; and, (iii) imaging a known-luminance targetplaced 60-m distant. However, integrating spheres are expen-sive (about few thousands euros), thus not fulfilling the low-cost calibration method requirement. Moreover, in order toreduce the effects of the environmental illumination on the60-m distant target (step iii), outdoors setups are probably lesssuitable, thus requiring large indoor spaces.

Therefore, considering both the depth of field of the de-veloped system [dmin,+∞[ estimated in (7) and the targetmeasurement uncertainty discussed in Section III, the proposedcalibration method makes use of a “single-setup” procedurebased on a certified Lambertian-diffuser reflectance standard(LRS) (model SRS-99-020, Labsphere, USA) (cost about twohundreds euros) placed 1.5 m away from CS.

A. Calibration Method

As previously reported in Section II, the relation between themeasurand L(x, y, z) and the system output g(r, c) depends onboth the optics of the system (5) and the pixel responsivity (6),thus theoretically requiring the estimation of the responsivity ofany of the pixels composing the CCD array.

CCD photosensor arrays show a certain degree of nonuni-form response of individual pixels, which could be described bytwo terms: (i) different dark signals (fixed pattern noise, FNP)and (ii) different responsivities (photoresponse nonuniformity,PRNU) [17]. However, FPN is generally remarkably low (about0.04% of the full grey value range), and PRNU is also verylow (typically the responsivity standard deviation is about0.3%) [17]. As a result, the shape of the calibration diagram ismainly determined by the residual cos4 θ behavior of the optics,whereas the pixel performances may be roughly considered thesame for all the pixels, thus basically representing a scale factorof the calibration diagram. Therefore, in order to reduce costand time of the calibration procedure and, considering the targetuncertainty, the calibration activities have been divided into twosteps: (i) analysis of the “on-axis” CS responsivity and linearityas a function of the exposure time t, thus placing LRS and CSin order to obtain the LRS image to form at the center of theCCD and changing the LRS luminance LLRS and t withoutnominally altering the LRS image position (Section V-B) and,(ii) analysis of the “off-axis” CS responsivity performed bymoving CS without nominally changing LLRS and t, thus, inturn, analyzing the residual cos4 θ behavior (Section V-C).

The geometry of the setup used for the calibration activitiesis shown in Fig. 5. LRS was uniformly illuminated by a75-W xenon lamp system (model 6251, Oriel, USA) at nominal

Fig. 5. Experimental setup used for the calibration activities. LRS was illu-minated by the xenon lamp that provides a uniform illuminance E0 onto theLambertian diffuser surface. The limiting aperture LA allows illuminating theLSR only, thus reducing the stray light collected by CS. CS can be moved in the(y, z) plane, thus allowing the calibration of CS for different object positions(“off-axis” calibration, Section V-C).

Fig. 6. Normalized spectrum of the used 75-W xenon lamp system (X). Thefigure also shows the normalized spectra of: the halogen lamp (H) (modelHL2000FHSA, Avantes, USA), the mercury lamp (M) (model 6282, Oriel,USA), and the solar spectral distribution at global Air Mass 1.5 model (ASTM).Spectra X, H, and M have been measured by using an optical spectrometer(PMA 11 C5966, Hamamatsu, Japan), whereas solar spectral distribution(ASTM) has been taken from ASTM G173-03 AM 1.5 global spectrumstandard [34].

normal incidence, and the illuminance E0 on its surface wasmeasured using a reference photometer PHO (model HD 9221,Delta OHM, Italy) (accuracy class ±0.15%, r.d.g. ±1 digit witha reference temperature T ∈ [20, 30]◦C—probe accuracy class±5%). As shown in Fig. 5, CS was placed at a 45◦ angle withrespect to the diffuser LRS, thus avoiding to collect the specularreflection components of real Lambertian diffusers [31].

Fig. 6 shows the normalized spectrum (X) of the used 75-Wxenon lamp system.

Measurements have been performed in a dark room (environ-mental illuminance lower than 1 lx), thus no field diaphragmhas been added in order to limit the CS f.o.v. In order to satisfythe far field condition and reduce blurring, LSR has been placedat a distance l of about 150 cm from CS. As shown in Figs. 5and 7, CS may be moved in the (y, z) plane, thus obtainingthe formation of the LRS image on different positions of the

Fig. 7. Snapshot acquired using CS; the picture has been acquired increasingthe environmental illuminance and tilting the LRS orthogonal to the CS opticalaxis. As shown in Fig. 5, by moving CS, it is possible to move the LRS imageon the CCD, thus allowing “off-axis” calibration. The white-line rectangleoverlapped to the LRS image underlines the pixels considered in the spatial-average grey level (gs−m) estimation.

CCD detector. Then, the object luminance LLRS was simplyestimated as [7], [17]

LLRS =ρ · E0

π(8)

where ρ = 0.9880 ± 0.005 is the LRS-diffuser meanreflectance-factor in the visible range.

The pixels grey level was assessed by performing a spa-tiotemporal average. Hence, in order to reduce the noise due to:(i) the xenon lamp illuminance E0 and LRS reflectivity spatialinhomogeneities and, (ii) possible dirt particles on the objective,the CCD cover glass and the IR cutoff filter, the grey level wasestimated averaging the acquired grey level matrix over a CCDarea of about Δc · Δr pixels, thus giving rise to spatial-averagegrey level gs−m(c0, r0)

gs−m(c0, r0) =1

Δc · Δr·

c0+12Δc∑

c=c0− 12Δc

⎡⎣ r0+

12Δr∑

r=r0− 12Δr

g(c, r)

⎤⎦ (9)

where (c0, r0) are the coordinates of the center of the consid-ered Δc · Δr pixels area.

Then, in order to reduce the noise due to: (i) the CCD,(ii) the reference photometer used for the E0 estimation and,(iii) the temporal fluctuations of the xenon lamp illuminanceE0, the obtained gs−m values were averaged over Nf frames,thus giving rise to the temporal-average grey level gt−m

gt−m(c0, r0) =1

Nf·

Nf∑i=1

g(i)s−m(c0, r0) (10)

where, g(i)s−m(c0, r0) is the gs−m(c0, r0) value relative to the ith

acquired frame.Analogously, the E0 value has been averaged over the

NE0 E0-values recorded during the acquisition of the Nf

frames

E0 =1

NE0

NE0∑i=1

E0. (11)

Fig. 8. Example of the barrel distortion introduced by the CS. The image hasbeen obtained imaging a grid composed by 4 by 4 cm squares, plotted on apaper placed at a distance of about 30 cm from the CS. The paper was backilluminated by an halogen lamp. The black shadow in the bottom of the imageis the optical head of the photometer. Rectangles (red in color version) havebeen added to facilitate the barrel distortion evaluation.

Even though the responsivity of photometers is known tochange depending on the temperature of their components,the environmental temperature was neither controlled nor mea-sured. Nevertheless, the CS, the reference photometer, andthe xenon lamp system have been set up in its measurementlocation with their power turned on for at least 1 h before themeasurements.

Of course, the obtained data are affected by uncertainty.Thus, the combined standard measurement uncertainties havebeen estimated applying the analytical procedure reported bythe International Standardization Organization [35]. The con-sidered sources of uncertainty were: (i) the uncertainty as-sociated with the LRS mean reflectance-factor and the PHOphotometer, and (ii) the experimental standard deviation of boththe gs−m, gt−m and E0 quantities (when applicable). Since theLseq estimation depends on the mean luminance Lij emittedby the sectors resulting from the image segmentation (Fig. 1),the proposed calibration method neglects optical aberrations.As shown in Fig. 8 the barrel distortion introduced by the CS isnot critical.

B. “On-Axis” Calibration

Responsivity and linearity have been estimated by fixing CSin order to obtain the formation of the LRS image at the centerof the CCD array. Then, different E0 values have been obtainedby varying the xenon lamp supply current.

Even though current supply variations are known to po-tentially alter the spectrum of the emitted light, such aspecthas been neglected (obviously, the closer the total spectralresponsivity of CS is to the V (λ), the lower is the influenceof the light source on the luminance results).

Moreover, to reduce the measurement time, the number offrames Nf was set to 1—gt−m = gs−m (also NE0 = 1 thusE0 = E0). Grey levels gs−m have been estimated performinga spatial average on a CCD area of about Δc = 20 and Δc =10 pixels.

As an example, the results obtained varying the nominalexposure time t from 2−8 s to 2−4 s are shown in Fig. 9.

Fig. 9. “On-axis” calibration curves. In order to make the graph more read-able, measurement uncertainties (mainly due to the PHO photometer) have beenomitted.

TABLE I“ON-AXIS” CALIBRATION FUNCTION AS A FUNCTION OF THE NOMINAL

EXPOSURE TIME t. THE SLOPES m(t) AND THE INTERCEPTS q(t) HAVE

BEEN ESTIMATED BY THE LINEAR REGRESSIONS OF THE MEASURED

DATA. FROM (6), m WAS EXPECTED TO BE A LINEAR FUNCTION OF t,WHEREAS EXPERIMENTAL RESULTS SHOW A LITTLE DEVIATION

FROM LINEARITY. SUCH DISCREPANCY IS PROBABLY DUE TO

LITTLE DEVIATIONS OF THE INTERNAL (ACTUAL) EXPOSURE

TIME WITH RESPECT TO THE NOMINAL ONE

The calibration functions have been estimated by the linearregression of the measured data; the obtained results and therelative coefficients of determination R2 are reported in Table I.

C. “Off-Axis” Calibration (Cosine Correction)

Activities have been performed accordingly to the calibrationmethod previously described in Section V-A.

“Off-axis” calibration requires the LRS image to be movedon the CCD area. Such, movement may be accomplished by:(i) translating LRS and/or CS in the (y, z) plane (Fig. 5),(ii) both moving CS along the y or z axis and rotating it alongits optical axis, or, as suggested by Fiorentin et al. [33], (iii) bytilting the CS along both the horizontal and vertical axes of itslens objective; our off-axis calibration has been performed byusing the first method.

Grey levels g(i)s−m have been estimated performing a spatial

average on a CCD area of about Δc = 20 and Δc = 10 pixels.Then, they have been averaged over Nf = 100 frames, thusobtaining the temporal-average grey levels gt−m(c0, r0). Inorder to further reduce the CCD noise, the CCD exposure timet was empirically set to fully exploiting the measuring intervalwithout reaching the sensor saturation (t = 2−6 s).

According to Section V-A, from (8) the CS responsivityRTOT−e has been estimated as

RTOT−e(c0, r0) =gt−m(c0, r0)

ρ · E0/π. (12)

TABLE IIESTIMATED RTOT−e VALUES EXPRESSED IN (m2/kcd). THE (0,0)

COORDINATE REFERS TO THE CENTER OF THE Δr · Δc PIXEL AREA IN

THE CENTRE OF THE CCD, WHEREAS OTHER COORDINATES REFER

TO THE CCD BOUNDARIES. THE RTOT−e VALUES HAVE BEEN

OBTAINED USING A PIXELS AREA Δc · Δr EQUALS TO 20 × 10,A NUMBER OF FRAMES Nf EQUAL TO 100, AND AN

EXPOSURE TIME t EQUAL TO 2−6 s

Fig. 10. Equalization function fEQ obtained from the least squareinterpolation. The • represent the mean of the normalized RTOT−e valuesrelative to the same nominal radial distance from the CCD center; the“error bars” show the experimental standard deviation of the mean. As anexample, the RN−1 value has been obtained averaging RTOT−e(c0 = 0,r0 = −230)/RTOT−e(c0 = 0, r0 = 0) and RTOT−e(c0 = 0, r0 =230)/RTOT−e(c0 = 0, r0 = 0).

As an example, Table II reports the (estimated) CS respon-sivity RTOT−e for different target positions.

It is important to notice that the RTOT−e(0, 0) value reportedin Table II is compatible with the [m(t = 2−6 s)]−1 valuereported in Table I.

The equalization function fEQ has been estimated from thedata reported in Table II.

Supposing axial symmetry of the optical system, the equal-ization function fEQ can be modeled as a 2-D even function.As a result, according to Goldman [36], the vignetting has beenmodeled as

fEQ(c, r)=α6 ·(c2+r2)3+α4 ·(c2+r2)2+α2 ·(c2+r2)+1.(13)

Then, the α6, α4, and α2 values have been estimated by aleast square interpolation.

In order to free the results from the used exposure time, thedata reported in Table II have been divided by RTOT−e(c0 =0, r0 = 0). As shown in Fig. 10, thanks to the axial symmetryhypothesis, the least square interpolation has been performedon the data obtained averaging the values relative to thesame radial distance from the origin of the pixel coordinatesystem—the CCD center. The obtained coefficients are reportedin Table III.

TABLE IIIα6, α4, AND α2 VALUES OBTAINED FROM THE LEAST SQUARE

INTERPOLATION OF (13) WITH THE R VALUES OF Fig. 10

From (5), the illuminance E ′ produced by an uniform objectluminance L was expected to decay as cos4 θ. However, ac-cordingly to Asada [37], since the value of cos4 θ falls rapidlyas the angle θ increase, lens system are designed to partiallycompensate the marginal image illuminance reduction due tothe cos4 θ law. Such is the reason why the fEQ functiondecreases less than the theoretical reduction expected by (5).

D. Overall Calibration

According to previous subsections, the luminances L of theobject whose image forms on the (c, r) pixel, can be estimatedas

L =g(c, r)

fEQ(c, r)· m(t) + q(t) (14)

where g(c, r) is the measured grey level and, fEQ, m(t), andq(t) values are reported in Tables I and III.

Obviously, as discussed in Section IV-B, lens objective set-tings and gain highly affect the measurement. Therefore, thevalues reported in Tables I and III are valid as long as no changeis made to both the lens objective settings and the CCD gain setduring the camera settings activities (Section IV-B).

VI. DISCUSSION AND CONCLUSION

In road tunnel lighting field, the real-time measurement ofthe veiling luminance may allow the estimation of the optimumluminance levels, thus increasing the drivers safety, avoidingenergy wasting and unjustified higher lighting costs or driverdazzle.

In this paper, we propose a low-cost system and calibrationmethod for veiling luminance estimation. The CS cost may beprobably further reduced by using even lower costing cameraand objective lens.

On the other hand, also calibration cost can be furtherreduced. Calibration costs mainly arise from the cost of:(i) the light source (calibration source), (ii) the photometer and,(iii) the calibration target (the reflectance standard).

Although, the light source cost is common to both opalglass and integrating sphere calibration methods, cheaper lightsources such as halogen lamp may be used. However, sinceno photometer can be perfectly matched to the V (λ) function,an error occurs whenever it measures light sources having aspectral distribution different from the calibration source [31].As shown in Fig. 6, the sources spectra may highly differfrom solar spectral distribution, thus particular care has to betaken when the agreement between the digital sensor spectralresponsivity Rλ(λ) and the spectral efficiency function V (λ)is poor.

Photometer cost mainly depends on the accuracy class (in-strumental uncertainty). Obviously, the instrumental measure-ment uncertainty has to be at least one order of magnitude lowerthan the target uncertainty. Hence, the use of an illuminancemeter has been a cost-driven choice, because commercial-grade illuminance meters are usually much more cheaper thanluminance meter. Nevertheless, (spot) luminance meters mayallow to avoid the need of Lambertian calibrated reflectancestandard, thus allowing a cost saving calibration target. Insteadof using (8), luminance meters perform a direct measure of theluminance, thus not only uncalibrated, but also non-Lambertiandiffuser can be used, hence possibly allowing outdoor calibra-tion by directly imaging real-world scenes. On the contrary,both illuminance and luminance meters require a calibrationtarget with spatially “uniform” reflectance if any spatial meanhave to be performed on the acquired pixels during calibrationactivities.

Cost saving Lambertian diffusers, e.g., white paper, can beprobably used both with illuminance and luminance meters.However, the estimation of the reflectance factor ρ of theuncalibrated reflectance standard may result in a nontrivial task.Nevertheless, as discussed in Section III, the target measure-ment uncertainty is “high” and data about the white paperspectral reflectivity are available in literature, i.e., [38], thuslower costing calibration targets can probably be used.

Most surfaces display a combination of specular and diffusebehaviors, with the specular becoming increasingly noticeablefor large angles of incidence and observation (measured fromthe surface normal), which is particularly the case for roadsurfaces [39]. Nevertheless, although the proposed calibrationactivities have been performed by using a LRS, the developedmeasuring system is only little affected by the actual opticalbehavior of the imaged objects. Such is due to the fact thatthe CS has been designed in order to provide a f.o.v. anal-ogous to the one of Fig. 1, thus, in turn, analogous to theone of an approaching driver. As a result, the calibrated CScollects almost the same luminance as it will be collected bythe approaching driver regardless to the actual object specularand diffuse behavior. Our calibration method makes use ofa LRS simply because the luminance developed by illumi-nated calibrated Lambertian diffuser can be easily estimatedusing (8).

The combined standard measurement uncertainties previ-ously estimated does not take into account: (i) optical aber-rations and, (ii) the spectrum of the collected light and themismatch between the shape of the CS spectral responsivityRTOT (λ) and the V (λ) photopic curve. As a result, the com-bined standard measurement uncertainties reported in Table IIhave to be considered as an estimation of the “intrinsic uncer-tainty” associated to the proposed low-cost calibration methodonly. According to the data reported in Appendix A, themeasurement uncertainty associated to the developed CS isone order of magnitude higher than the standard measurementuncertainties reported in Table II.

Optical aberrations basically does not change the overallenergy (power) at the image plane which is defined by thelens Etendue, but they affect the radiant energy (power) spatialdistribution.

Since the Lseq estimation depends on the mean luminancesresulting from the image segmentation, spherical distortionsare probably the optical aberrations that mainly affect the Lseq

estimation. According to Fig. 8, spherical distortions have beenarbitrarily considered negligible, thus its contribute has beenexcluded from the calibration activities.

On the other hand, calibration has been performed by usinga xenon lamp (calibration source, Fig. 6). If the spectrum of thecollected light is equal to the spectrum of the calibration source,differences between the shape of the CS spectral responsivityRTOT (λ) and the V (λ) does not theoretically affect the Lseq

estimation. However, real-world scenes give in general rise toan image that is composed by several different spectra. More-over, solar spectrum is known to vary during the day. Therefore,in order to perform an estimation of the object luminance, theshape of the overall spectral responsivity RTOT (λ) of the CS(optics, filter, and photodetector) has to be as close as possibleto the photopic CIE Standard Observer function V (λ) [7],[17], [31].

Monochrome camera systems (optics and CCD detector)are designed in order to provide grey levels that represent theobjects as close as possible as they would have been seen by anobserver. Furthermore, according to Flannagan [40], disabilityglare is mainly determined by the illuminance at the eye,whereas the spectrum of the received light is almost unimpor-tant. Moreover, as previously stated the Lv estimation processallows high uncertainty thus allowing us to arbitrary supposethe spectral match between the CS responsivity RTOT (λ) andthe V (λ) function shape to be adequate for the application.Obviously, no photometers can be matched perfectly to theV (λ) function and the degree of the spectral mismatch withthe V (λ) function may be evaluated by the term f ′

1 given in theCIE Publication 69 [41] (the term f ′

1 is an evaluation index andcannot be used for correction purposes).

Even though, it is known that the V (λ) underestimates thephotoreceptor responsivity in the UV region by a factor ofnearly ten [27] (Section III); from Fig. 4, it is easy to noticethat the spectral responsivity of proposed CS deviates fromthe spectral efficiency function V (λ) (particularly in the UVregion). As reported in Appendix A, the f ′

1 associated to theuse of both the ICX098BL greyscale CCD and 486 filter can beroughly estimated as f ′

1−ICX098BL ≈ 80%.According to Ohno [31], it is recommended that a standard

photometer has a f ′1 value of less than 3%. It is difficult to

quantify the definitional uncertainty associated with the usageof the model of measurement defined by UNI 11095 [5] andreported in (4). However, from the discussion in Section III,such definitional uncertainty is probably not lower than sometens of percent. Moreover, as previously stated, the UNI 11095standard allows to estimated the Lesq by using the tabulateddata only, thus allowing high target uncertainty. Therefore,considering such target measurement uncertainty and the low-cost target, the ICX098BL greyscale CCD equipped with the486 UV-IR-CUT filter has been considered suitable for theapplication.

The proposed low-cost calibration method is basically in-dependent of the chosen camera and filter. As a result, pho-topic filter can be used if better spectral matching is required.

Moreover, better spectral matching can be achieved by simplyusing the green signal provided by RGB cameras (Fig. 4). RGBcameras are based on the Bayer filter array, thus generallyresulting more expensive than greyscale camera. Moreover,the use of the Bayer filter array both introduces a source ofuncertainty on the single-pixel spectral responsivity and halvesthe number of available pixels. Furthermore, as shown in Fig. 4,in order to use the green signal provided by a RGB camera, anIR-CUT optical filter is still generally required.

Nevertheless, according to Appendix A, the f ′1 value as-

sociated to the use of both the green signal provided by theICX098BQ CCD and the 486 filter can be roughly estimated asf ′1−ICX098BQ ≈ 40%.

Therefore, if better spectral matching is required, the usageof the green signal provided by (IR-filtered) RGB cameras maybe a proper choice.

Both the above f ′1 values (40% and 80%) may appear

extremely high, however, as previously stated intra- and in-terindividual variability lead to great definitional uncertainty.Moreover, the sun at the zenith may give rise to a luminanceof about 1.6 · 105 cd/cm2 that reduces to 6 · 102 cd/cm2 once itreaches the horizon or even 4 · 10−3 cd/cm2 in dark cloudy days[30]. Therefore, the proposed system can provide much betterperformances than the usage of tabulated data.

APPENDIX AEVALUATION OF THE MATCH BETWEEN THE CAMERA

SPECTRAL RESPONSIVITY AND V (λ)

As previously discussed, the f ′1 given in the CIE Publication

69 [41] can be used in order to obtain an estimation of degreeof the spectral mismatch with V (λ).

According to CIE 69 [41], the f ′1 can be calculated as

f ′1 =

∫λ |R∗

rel(λ) − V (λ)| dλ∫λ V (λ) dλ

· 100 (15)

where

R∗rel(λ) =

∫λ SA(λ) · V (λ) dλ∫

λ SA(λ) · Rrel(λ) dλ· Rrel(λ). (16)

In (16), SA(λ) is the relative spectral power distributionof CIE Standard Illuminant A [42] and, Rrel(λ) is the (ra-diometric) relative (normalized) spectral responsivity of theinvestigated system.

Then, the normalized spectral responsivity of the investigatedcamera system can be estimated as

Rrel(λ) = Rλ(λ) · Toptics (17)

where Rλ(λ) is the digital sensor (radiometric) normalizedspectral responsivity and Toptics is the overall optical systemtransmittance.

Our system makes use of the UV-IR-CUT optical filter model486 (The Imaging Source, Germany), thus supposing Toptics ≈T486, the the f ′

1 associated to the use of both the ICX098BLgreyscale CCD and the 486 filter can be roughly estimated

Fig. 11. R∗rel(λ) obtained applying (16) to the systems composed by:

(i) ICX098BL greyscale CCD and the 486 filter (R∗rel−ICX098BL)

and (ii) green signal of the ICX098BQ RGB CCD and the 486 filter(R∗

rel−ICX098BQ). Data have been taken from the CCDs and filter datasheet,respectively.

as f ′1−ICX098BL ≈ 80%. As an example, the f ′

1 associated tothe use of both the green signal provided by the ICX098BQCCD and the 486 filter (Fig. 4) can be roughly estimated asf ′1−ICX098BQ ≈ 40%. Fig. 11 shows the R∗

rel(λ) obtained byusing (16).

From Fig. 4, it would have been thought thatf ′1−ICX098BLQ � f ′

1−ICX098BL. However, as well as anerror occurs when a photometer measures a light source havinga spectral distribution different from the calibration source, thef ′1 value estimates how a photodetector differs in “perceiving”

a certain light source—Illuminant A—with respect to the V (λ).Due to the low spectral power distribution of the Illuminant Ain the UV region, the great deviation of Rλ−ICX · T486 fromV (λ) in the UV region little affects the f ′

1 value.

ACKNOWLEDGMENT

The authors wish to thank Professors Costantino Grana andRita Cucchiara for the valuable suggestions and Andrea Nosarifor his contribution during the experimental activities.

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Stefano Cattini (M’09) received the (First-ClassHonors) degree in electronic engineering and thePh.D. degree on topics relating to the optoelectronicinstrumentation and measurements in biomedicalscience and clinical applications from the Universityof Modena and Reggio Emilia, Modena, Italy, in2005 and 2009, respectively.

He is currently with the University of Modenaand Reggio Emilia. His activities are mainly fo-cused on the development of instrumentation formeasurements in biomedical science and clinical and

industrial applications, ranging from photon correlation spectroscopy sensors,interferometric sensors, to magnetic field sensors.

Luigi Rovati (M’92) received the (First-Class Hon-ors) degree in electronic engineering and Ph.D. de-gree in electronic engineering and computer sciencefrom the University of Pavia, Pavia, Italy, in 1989and 1994, respectively.

From 1995 to 2001, he was Researcher and As-sistant Professor at the Department of Electronicsfor the Automation, University of Brescia, Brescia,Italy. He joined the Department of Information En-gineering, University of Modena and Reggio Emilia,Modena, Italy, in 2001, where he presently is Asso-

ciate Professor of Electronic Instrumentation and Measurement Science (09/E4:MISURE). The published papers production (more than 150 publications)testifies the level of the developed activity. He was involved in various researchprojects between University and industrial partners. His research activities havebeen toward the study and the development of low-noise, high-performance,innovative instrumentations. The present involvement of his research activity isrelated to the design of innovative biomedical instrumentation mainly orientedto ophthalmic diagnostic systems.