Research Article Multifeature Fusion Vehicle Detection ...integral, the vehicle symmetry similarity measure and the HOG + A daBoost feature similarity measure are de ned. Finally,

Research ArticleMultifeature Fusion Vehicle Detection Algorithm Based onChoquet Integral

Wenhui Li,1,2,3 Peixun Liu,1 Ying Wang,1,2 and Hongyin Ni1

1 College of Computer Science and Technology, Jilin University, Changchun 130012, China2 State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China3 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University,Changchun 130012, China

Correspondence should be addressed to Ying Wang; wangying [email protected]

Received 13 May 2014; Accepted 25 June 2014; Published 24 July 2014

Academic Editor: Weichao Sun

Copyright © 2014 Wenhui Li et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind SpotDetection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness ofvehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicledetection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: featuresimilarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicledetectionmethod, and then vehicle taillight feature similaritymeasure is defined. Second, combiningwith the definition of Choquetintegral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, thesethree features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, theexperimental results show that our method has achieved effective and robust multivehicle detection in complicated environments.Ourmethod can not only improve the detection rate but also reduce the false alarm rate, whichmeets the engineering requirementsof Advanced Driving Assistance Systems (ADAS).

1. Introduction

As an important part of the intelligent transportation system(ITS), the Advanced Driving Assistance Systems (ADAS)can significantly improve the driving safety. Forward Col-lision Warning Systems (FCWS) and Blind Spot DetectionSystems (BSDS) are principal portions of ADAS, and theirperformance depends on the real-time capability, accuracy,and robustness of the vehicle detection method. Recently,with the increasing maturity of visual sensors, vision-basedvehicle detection has become a hot topic in the field ofintelligent vehicle. There are plenty of approaches proposedfor the day time vehicle detection. These methods can bedivided into the following categories: methods based on priorknowledge, such as shadow-based [1, 2], taillight-based [1,2], horizontal (vertical) edge-based [2–4], and symmetry-based vehicle detection method [2]; methods based onstereo vision; this type of method detects vehicles by using

the three-dimensional information. The most widely usedmethods are inverse perspective transformation (IPM) basedmethod and disparity map based method [1]; template-basedmethods use predefined patterns of vehicle class and performcorrelation between the image and the template [1]; the maindetection steps of appearance-based methods are as follows:the appropriate descriptors are first used for representingvehicles in the image; then the machine learning methodsare used to train these descriptors. Much processes havebeen made in appearance-based vehicle detection, such asalgorithm based on HOG + AdaBoost [5], Haar + HMM[6], Haar + AdaBoost [7–9], HOG + SVM [10], PCA – ICA+ GMM [11], and minimum Mahalanobis distance classifier[12]. The method based on the motion information detectsvehicles by using the motion information between vehiclesand scenes, such as finding out vehicles by calculating thechange of optical flow information which is caused by therelative motion of vehicles or scenes [13].

Hindawi Publishing CorporationJournal of Applied MathematicsVolume 2014, Article ID 701058, 11 pageshttp://dx.doi.org/10.1155/2014/701058

2 Journal of Applied Mathematics

To improve the accuracy of vehicle detection methods,several of above methods are combined together to detectvehicles. Lin et al. [3] applied the SURF and edge featuresto represent the vehicle and, combining with probabilisticmethods, their methods have achieved vehicle detection inthe blind spot area. Chen et al. [6] first used a road modellingmethod to confine detection regions, and then Haar-likefeatures and eigencolours were used for detecting vehicles.Finally, a tackling method was used. Tehrani Niknejad et al.[10] proposed a deformable vehiclemodel based onHOG fea-ture; the method can achieve the adaptive threshold vehicledetection under urban roads. Wang and Lien [11] proposed avehicle detectionmethod based on a statistical model of localfeature. They applied the public dataset Caltech Cars (Rear)[16] to test their method. Alonso et al. [12] proposed a vehicledetection method based on multidimensional classification.They represented vehicles in form of rectangular subregionsbased on the robust classification of features vectors resultof a combination of multiple morphological vehicle features.Their method can detect vehicles with very different aspect-ratio, color, and size. Chang and Cho [8] presented a vehicledetection algorithm based on combination of Haar featureand online boosting. Their algorithm has realized vehicledetection in various environments. Sivaraman and Trivedi[9] proposed an active-learning framework based on Haarfeature and AdaBoost for vehicle detection on the highway.Jazayeri et al. [13] proposed an optical flow and hiddenMarkov model-based vehicle detection method which modethe locations andmotion information of vehicles in the imagelevel. Their method can deal with the vehicle identificationproblem under the scene of changing illumination andenvironment well.

Vehicle detection method based on a single feature canquickly detect vehicles in images. However, using singlefeature methodmay cause a lot of false alarms, because singlefeature only describes one certain characteristic of vehicles.Most of the appearance-based vehicle detection methods’performance excessively depends on the number and scaleof training samples. Various samples in different situationsare needed to generate more powerful classifiers. In addition,detecting vehicles in images using appearance-based meth-ods which has to scan the whole image requires excessivecalculation and cannot meet the real-time requirement ofFCW. To solve the above-mentioned problem, a widely usedmethod is multifeature fusion which combines several singlefeature-based algorithms together by using voting method.This can significantly reduce the false alarm rate, but thedetection rate is reduced either. In recent years, mathematicaltheory has been widely used for improving the performanceof complex vehicle systems. Much process has been made inthe field of mathematical modeling and control methods [17–23], such as adaptive back stepping control for active suspen-sion systems with hard constraints [17], saturated adaptiverobust control for active suspension systems [18], and adap-tive robust vibration control of full-car active suspensionswith electrohydraulic actuators [19]. Choquet integral is awidely used method in data fusion [24–26]; it can seek themaximum consistency of decision from the consistency andconflict detection results of multiple features. To improve

the performance of vehicle detection algorithm and to solveproblems above, we propose a multifeature fusion vehicledetection algorithm based on Choquet integral. Experimentresults show that our multifeature fusion method will notonly improve the detection rate but also reduce the false alarmrate.

Figure 1 illustrates the workflow of our approach. Therest of the paper is organized as follows. Section 2 brieflyintroduces the shadow-based vehicle region of interest (ROI)detection method. In Section 3, vehicle taillight feature sim-ilarity measure, vehicle symmetry feature similarity mea-sure, and HOG + AdaBoost feature similarity measure arepresented, respectively. Then our multifeature fusion vehicledetection algorithm based on Choquet integral is introducedin Section 4. Experiment results for the proposedmethod areshown in Section 5; finally Section 6 draws conclusions.

2. Shadow-Based Vehicle ROI Detection

The shadow-based vehicle detection algorithm is usuallyapplied to extract the vehicle ROIs in the whole images forreducing computation complexity [1]. We have developeda shadow-based vehicle detection method, and the basicprinciple of the method is that regions underneath vehiclesare distinctly darker than any other regions on an asphaltroad. The grayscale of pixels in shadow regions is muchlower than that in any other regions in the same image.Grayscale histogram (GH) can reflect the whole imagegrayscale distribution well. The grayscale of vehicle shadowpixels belongs to the lower parts of GH. So we can detectthe shadow regions underneath vehicles by segmenting GHwith a threshold th BW. Figure 2(a) is a vehicle image fromCaltech Cars (Rear) [27]. Black regions in Figure 2(b) areshadow regions segmented by setting th BW to 0.1.The greenlines in Figure 2(c) are vehicle shadow lines detected byshadow-based vehicle detection method.

3. Feature Similarity Measure

To make full use of the Choquet integral in our mul-tifeature fusion vehicle detection framework, each singlefeature should be first represented in form of fuzzificationbefore calculating the Choquet integral. After this phase, thealgorithm can fuzz the output of each single feature; then theresult can be determined by using the fuzzy judgment insteadof direct judgment. Therefore, in this section, we detailedlyintroduce three feature-based vehicle detection methods andtheir feature similarity measure functions.

3.1. Vehicle Taillight Feature Similarity Measure. The redtaillights and braking lights are important features for detect-ing the rear-view vehicle. Taillight-based feature providesan important criterion for our multifeature fusion vehicledetection framework. The RGB components of pixels intaillight regions are obviously different from the other partsof vehicle ROI (except red cars). Therefore, by followingthis rule, we present a similarity measure method based oncolor feature of vehicle taillights. First, taillight regions in

Journal of Applied Mathematics 3

Vehicle taillightfeature similarity

measure

Vehicle symmetryfeature similarity

measure

classifier similaritymeasure

ROI detection byusing shadow-based vehicle

detection method

fusion based onChoquet integral

Vehicle

Y

N

If the value ofChoquet integral is greater

than threshold

Notvehicle

Inputimages

HOG + AdaBoost

Multifeature

Figure 1: Framework of our approach.

(a) (b)

(c)

Figure 2: Result of shadow-based vehicle ROI detection method.

the vehicle ROI are detected by threshold value segmentationmethod. The key threshold of method can be acquired byanalyzing RGB components from images of taillights and theother parts of vehicle. The collection of images for settingthe threshold should be large enough and includes differentvehicles and various scenes. We acquire the 𝑅, 𝐺, and 𝐵components distributions by analyzing the public collection[27] and images captured by our camera. As shown inFigure 3(b), differences between the 𝑅 component and the𝐺 component of the other regions of vehicle are mainlydistributed on the range of [1, 31]. It is different from thevalues of |𝑅 − 𝐺| in taillight regions illustrated in Figure 3(a);therefore, the taillight regions of vehicle ROI can be detectedby setting a certain thresholdTh Taillight:

𝐼Taillight (𝑥, 𝑦)

= {255, if 𝑅 (𝑥, 𝑦) − 𝐺 (𝑥, 𝑦)

≥ Th Taillight0, otherwise.

(1)

Figure 4(a) is the vehicle ROI detected by the shadow-based vehicle detection method. Figure 4(b) is the binaryimage of taillights detected by employing (1) on the vehicleROI. Canny-based edge detection method is used to detectthe edges of taillights in Figure 4(b), and Figure 4(c) is theedge image of Figure 4(b). Then the connected domains inFigure 4(c) are extracted. The input images of connecteddomain extraction method are binary and edge imagewhich are illustrated as Figures 4(b) and 4(c), respectively.


02468101214161820

0–15 16–31 32–47 48–63 64–79 80–95 96–111 112–127

Num

ber o

f pix

els

Statistical interval

|R − G|

|G − B|

|R − B|

×104

>128

(a)

02468101214161820

0–15 16–31 32–47 48–63 64–79 80–95 96–111 112–127

Num

ber o

f pix

els

Statistical interval

|R − G|

|G − B|

|R − B|

×104

>128

(b)

Figure 3: Comparison of RGB components between taillights and the other parts of vehicles.

(a) (b)

(c) (d)

Figure 4: Extraction of taillight areas.

Finally, the minimum circumscribed rectangles (MCR) ofconnection domains are calculated. The detected MCRs areillustrated as the red rectangles in Figure 4(d).

Each MCR of connected domain is represented bythe left top point MinPoint

𝑖(𝑥, 𝑦) and the right top point

MaxPoint𝑖(𝑥, 𝑦) of MCR. Two left top points in vehicle ROI

can form a straight line; the slope of straight line is defined as

𝐾𝑖

condomains =𝑦𝑖

minpoint − 𝑦𝑖−1

minpoint

𝑥𝑖

minpoint − 𝑥𝑖−1

minpoint. (2)


The distance between each MCR is represented as (3). Thetwo taillights of vehicle are usually on a horizontal line, andthe thresholds th 𝐿 and th 𝐻 can get rid of the straight linesthat are not horizontal or almost horizontal:

width taillights

=

{{

{{

{

Max (𝑥𝑖maxpoint − 𝑥𝑖−1

minpoint) ,

if th 𝐿 ≤ 𝐾𝑖condomains ≤ th 𝐻,0, otherwise.

(3)

Definition 1. The taillight feature similarity measure function𝐶tailCoeff is defined as

𝐶tailCoeff =width taillightswidth ROI

. (4)

3.2. Vehicle Symmetry Feature Similarity Measure. The sym-metry measure is a statistic to describe the symmetry oftarget. Vehicles are obviously symmetrical objects; therefore,we use the symmetry feature as a similarity measure inour algorithm. According to the symmetry-based methoddescribed in [28], we use the symmetry measure methodbased on normalized entropy to calculate the symmetry valueof each vehicle ROI. The symmetry measure is describedas (5), where 𝑆(𝑥

𝑠) is the symmetry measure of target. 𝐸(𝑙)

is the information entropy, which is also the mathematicalexpectation of information content. 𝐸

𝑚is the max value of

information entropy. Consider

𝑠𝑔

=[(𝑆 (𝑥

𝑠) + 1) /2 + 𝐸 (𝑙) /𝐸𝑚]

2

=𝑆 (𝑥𝑠) × 𝐸𝑚

+ 2 × 𝐸 (𝑙) + 𝐸𝑚

4 × 𝐸𝑚

.

(5)

Definition 2. The symmetry feature similarity measure func-tion 𝐶symCoeff is defined as

𝐶symCoeff = {𝑠𝑔, 0 ≤ 𝑠

𝑔≤ 1,

1, 𝑠𝑔

> 1.(6)

3.3. HOG and AdaBoost Classifier Feature Similarity Measure.The histogram of oriented gradient (HOG) is a descriptor offeaturewhich has beenwidely used in object detection. Zhu etal. [29] introduced an efficient pedestrian detection methodbased on HOG and AdaBoost. In our previous work, we useHOG feature to detect pedestrian [14]. The HOG feature isrepresented by calculating the histogram of oriented gradientof local region in the image. First, the image is divided into aplurality of grids according to a certain size; these grids arecalled BLOCK which are illustrated as in Figure 5(a). Theneach BLOCK is divided into four regions which are calledCELL. Each CELL projects an orientation-based histogramwhich includes nine bins. In this histogram, the horizontalordinate is a range of direction angles which divide 180∘into nine equal parts, and the vertical coordinates are anaccumulation of each angle range. Finally, a 36D featurevector named BLOCK is formed. Due to the strong edge

feature of vehicles, we employ the HOG feature to representvehicles; then the AdaBoost-based algorithm [30] is appliedto generate weak classifiers.

In this paper, the training samples of generating HOG +AdaBoost classifiers are images captured from actual drivingenvironments. Vehicle regions of these images are positivesamples, and other regions of images are negative samples.The amount of positive samples andnegative samples are both10000. These samples are normalized to the same size (30 ×30). Screenshots of samples are shown in Figures 5(b) and5(c). There are two phases to employ the HOG + AdaBoostclassifier and the training and the detection phase. In thetraining phase, we extract HOG features by applying CELLsize of 5 × 5, 10 × 10, and 15 × 15, respectively; the scanningstep size is three pixels, and the weak classifiers are selectedby AdaBoost algorithm. After training, we use the samples(positive 10000 and negative 10000) which are different fromthat of the training phase to test the weak classifiers.TheROCcurves of HOG + AdaBoost algorithm under three differentCELL sizes are illustrated as in Figure 6; the performance ofHOG + AdaBoost classifiers whose CELL size is 15 × 15 is thebest among these three types; therefore, we set the CELL sizeto 15 × 15 in our further experiments.

To enhance the performance of HOG + AdaBoost clas-sifiers, inspired by method in [9], the active-learning basedHOG + AdaBoost framework is used by following the stepsin the Active-Learning Framework. The advantage of thisframework is that you are only adding negative samples thatwould otherwise be causing false positives. There is no pointin adding more negative samples that are handled by theoriginal training anyways.

Active-Learning Framework.

Step 1. Train HOG + AdaBoost classifiers using the10000 positive samples and 30000 negative samples.Step 2. Run the algorithm by using well-trainedHOG + AdaBoost classifiers on a large video set (notthe training set from Step 1).Step 3. Any false positives from the run in Step 2 canbe put in the negative set.Step 4. Retrain the algorithm using the original truepositive set and the updated negative set (negativesfrom both Step 1 and Step 3).Step 5. This can be repeated as many times as appro-priate, using new video on each iteration.

In detection phase, each vehicle ROI detected by shadow-based vehicle detection method is resized to the same size ofthe training sample; the HOG feature is extracted in the sameway of training phase. Then use the well-trained classifiersto identify the vehicle ROI; the classification value of eachvehicle ROI is calculated by

hogadbCoeff =𝑇

∑

𝑖=1

𝛼𝑖

⋅ ℎbase𝑖

⋅ th strong. (7)

Most ofAdaBoost-based object detectionmethods decidewhether the ROI is object or interference by judging whether


Block

Cell

(a) (b) (c)

Figure 5: Some samples of training dataset.

−0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7−0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

5 × 5

10 × 10

15 × 15

Det

ectio

n ra

ter+

False alarm rate r−

Figure 6: ROC curves of HOG + AdaBoost algorithm under threedifferent HOG CELL sizes.

00.10.20.30.40.5

Prop

ortio

n

hogadbCoff

VehicleOthers

1∼1.5

0.5∼1

0∼0.5

−0.5∼0

−1∼−0.5

−1.5∼−1 2

−−2.5∼

<−2.5

−2∼−1.5

Figure 7: Statistic distribution of hogadbCoeff belonging to vehiclesand interferences.

the symbol of the hogadbCoeff is positive or negative. Thisway is not suitable to employ the HOG and AdaBoost-basedclassifiers in our Choquet integral-based multifeature fusionvehicle detection framework. To represent the hogadbCoeffin form of probability, we first test the well-trained classifiers

Table 1: Mapping table between 𝐶𝑠𝑦𝑚𝐶𝑜𝑒𝑓𝑓

and hogadbCoeff.

hogadCoff 1.5ChogadbCoff 0.85 0.9 0.95 0.99 1

by using the testing sample set which is different from thetraining sample set. And then the statistic distribution ofhogadbCoeff is calculated. Finally themapping table betweenthe HOG and AdaBoost classifier feature similarity measurevalue 𝐶hogadbCoeff and the hogadbCoeff is formed. The statis-tic distributions of hogadbCoeff belonging to vehicles andinterferences are illustrated in Figure 7; we use the algorithmprecision 𝑝+ corresponding to interval of hogadbCoeff to bethe 𝐶hogadbCoeff; the precision is defined as (16) in this paper;the mapping table is created as Table 1.

Definition 3. TheHOG + AdaBoost classifier feature similar-ity measure function 𝐶hogadbCoeff is defined as Table 1.

4. Multifeature Fusion Vehicle DetectionAlgorithm Based on Choquet Integral

In this paper, fuzzy integral theory is applied to vehicledetection in complex scenarios. First, the basic theory ofChoquet integral is introduced here. And then the fuzzymeasure of each feature is defined. Finally, the features oftaillight, symmetry, andHOG+AdaBoost classifier are fusedby Choquet integral of fuzzy theory. The brief conceptsof Choquet integral and the fuzzy measure used in ouralgorithm are followed from the concepts in [24–26].

Definition 4. Let 𝑋 be a finite set, and 𝑌 is a power set whichis composed of subsets of 𝑋, 𝑔 : 𝑌 → [0, ∞] is the mappingfunction from the power set 𝑌 to the range of [0, ∞]. If 𝑔satisfies the following three conditions, 𝑔 is a fuzzy measureon 𝑌.

(1) Boundedness: 𝑔(𝜑) = 0.

(2) Monotonicity: ∀𝐴, 𝐵 ∈ 𝑌, if𝐴 ⊆ 𝐵, then 𝑔(𝐴) ≤ 𝑔(𝐵).


(3) Continuity: if ∀𝐴𝑛

∈ 𝑌, and {𝐴𝑖

| 𝑖 ∈ [1, +∞]} ismonotonous. This is also represented in the form of𝐴1

⊆ 𝐴2

⊆ ⋅ ⋅ ⋅ ⊆ 𝐴𝑛

⋅ ⋅ ⋅ or 𝐴1

⊇ 𝐴2

⊇ ⋅ ⋅ ⋅ ⊇ 𝐴𝑛

⋅ ⋅ ⋅ ,then lim

𝑖→∞𝑔(𝐴𝑖) = 𝑔(lim

𝑖→∞𝐴𝑖).

The fuzzymeasurewhich is widely applied inmultifeaturefusion is the regular fuzzy measure: if 𝑋 ∈ 𝑌 and 𝑔(𝑋) = 1,the fuzzy measure 𝑔 is regular.

Definition 5. If the fuzzy measure satisfies the followingconditions: ∀𝐴, 𝐵 ∈ 𝑌, 𝐴 ∩ 𝐵 = 𝜑, if there exists a constantvalue𝜆,𝜆 > −1 satisfying𝑔(𝐴∪𝐵) = 𝑔(𝐴)+𝑔(𝐵)+𝜆𝑔(𝐴)𝑔(𝐵),then 𝑔 is a 𝜆-fuzzy measure. 𝜆 can be calculated by (8), where𝑔𝑖

= 𝑔({𝑥𝑖}); it is used to indicate the importance of a single

feature classifier for the final evaluation, where 𝑥𝑖

∈ 𝑋 =

{𝑥1, 𝑥2, . . . , 𝑥

𝑛}. Consider

1 + 𝜆 =

𝑛

∏

𝑖=1

(1 + 𝜆 × 𝑔𝑖) . (8)

Definition 6. 𝑓 : 𝑋 → [0, 1] is a nonnegative functiondefined on 𝑋, 𝑔 is a fuzzy measure defined on power set 𝑌,and then Choquet integral of function 𝑓 on 𝑋 with respect tofuzzy measure is defined by

∫ 𝑓𝑑𝑔 = ∫

∞

0

𝑔 (𝑌𝜇) 𝑑𝜇, (9)

where 𝑌𝜇

= {𝑥 | 𝑓(𝑥) ≥ 𝜇, 𝑥 ∈ 𝑋}, 𝜇 ∈ [0, ∞); the mainidea of (9) is determining the value of Choquet integral usingRiemann integral by an infinite approximation method. Thedefinition of Choquet integral is as follows when 𝑋 is a finiteset:

∫ 𝑓𝑑𝑔 =

𝑛

∑

𝑖=1

[𝑓 (𝑥𝜃(𝑖)

) − 𝑓 (𝑥𝜃( 𝑖−1)

)] 𝑔 (𝐾𝜃(𝑖)

) , (10)

where 𝜃 is a permutation of the indices such that

0 = 𝑓 (𝑥𝜃(0)

) ≤ 𝑓 (𝑥𝜃(1)

) ≤ ⋅ ⋅ ⋅ 𝑓 (𝑥𝜃(𝑛)

) ≤ 1,

𝐾𝜃(𝑖)

= {𝑥𝜃(𝑖)

, 𝑥𝜃(𝑖+1)

, 𝑥𝜃(𝑖+2)

, . . . , 𝑥𝜃(𝑖+𝑛)

} , 𝑖 = 1, 2, . . . , 𝑛.

(11)

When fuzzy measure 𝑔 is a 𝜆-fuzzy measure, any subsetis defined by

𝑔 (𝐾𝜃(1)

) = 𝑔 ({𝑥𝜃(1)

}) = 𝑔𝜃(1)

,

𝑔 ({𝑥𝜃(𝑖)

}) = 𝑔𝜃(𝑖)

,

𝑔 (𝐾𝜃(𝑖)

) = 𝑔𝜃(𝑖)

+ 𝑔 (𝐾𝜃(𝑖−1)

)

+ 𝜆𝑔𝜃(𝑖)

𝑔 (𝐾𝜃(𝑖−1)

) , 𝑖 = 2, . . . , 𝑛.

(12)

To apply the Choquet integral to detect vehicles incomplex environments, 𝑂 is first initialized as the vehicleROI detected by shadow-based vehicle detection algorithm.𝐹 = {vechile, interfernce} is a classification framework. 𝑋 ={𝑥1, 𝑥2, 𝑥3} is the feature set for detecting vehicle, where 𝑥

1,

𝑥2, and 𝑥

3represent the vehicle symmetry feature, the vehicle

00.20.40.60.81

1.2

tempchoquet

Region of interest

VehicleInterference

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93

Figure 8: Comparison of temp choquet between the vehicle and theinterference.

taillight feature, and vehicle HOG + AdaBoost classifiersfeature, respectively. Let 𝑔 : 𝑋 → [0, 1] be the fuzzy densityof vehicle ROI 𝑂 belonging to the class 𝐹

𝑖; define 𝑔(𝑥

𝑖) to be

the degree of importance of the feature𝑥𝑖in decidingwhether

vehicle ROI is vehicle or interference. Define 𝑔(𝑥1) = 𝑔({𝑥

1}),

𝑔(𝑥2) = 𝑔({𝑥

2}), and 𝑔(𝑥

3) = 𝑔({𝑥

3}); the higher the 𝑔(𝑥

𝑖) is,

the more important the feature 𝑥𝑖is. The fuzzy function 𝑓 is

defined in [0, 1] so that 𝑓(𝑥1) = 𝐶tailCoeff, 𝑓(𝑥2) = 𝐶symCoeff,

and 𝑓(𝑥3) = 𝐶hogadbCoeff. To calculate the value of Choquet

integral for each vehicle ROI, the features 𝑥𝑖in the set 𝑋 are

needed to be rearranged with respect to the order 𝑓(𝑥1) ≤

𝑓(𝑥2) ≤ 𝑓(𝑥

3).

Main steps of our multifeature fusion vehicle detectionalgorithm based on Choquet integral are as follows.

Multifeature Fusion Vehicle Detection Algorithm Based onChoquet Integral.

Step 1. Calculate the fuzzy measure of each feature.We test each feature-based vehicle detection methodon the same vehicle sample set, and, according to (16),the precision of each vehicle detection method can beacquired. Let the precision 𝑝+ be the fuzzy measure 𝑔corresponding to each feature-based method.Step 2. Calculate 𝜆 by (8).Step 3. Estimate the 𝜆-fuzzy measure by (12).Step 4. The Choquet integral value of each ROItemp choquet can be calculated by (10) combiningwith three feature similarity measures.Step 5. Decide whether the vehicle ROI is vehicleaccording to (13). As it is illustrated in Figure 8,the temp choquet belonging to the vehicle and thatbelonging to the interference are much more differ-ent; the threshold Th vehicle can be set according toFigure 8:

isVehicle = {1, if temp choquet > Th vechicle,0, otherwise.

(13)

5. Experiment Results

To verify the performance of the algorithm, experimentalplatform has been built in c using OpenCV 1.0 library


0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.000.3

0.4

0.5

0.6

0.7

0.8

Threshold

TaillightSymmetry

Prec

ision

p+

HOG + AdaBoost

Figure 9: Algorithm precision under various thresholds.

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.500.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Choquet fusionTaillightSymmetry

Det

ectio

n ra

ter+

False alarm rate r−

HOG + AdaBoost

Figure 10: Algorithm ROC curves.

and Visual Studio 2010. The vehicle detection algorithm isperformed on an Intel Core i7-3770GHZPC. A part of vehicleimages for testing are from the public test library CaltechCars (Rear) [27]. The rest of vehicle images are captured inthe real environments (parking lot and urban road) by usingDEWETRON DEWE2-M4 (camera: DEWE-CAM-01, lens:computar M3Z1228C) and SAMSUNG GT-S7562 camera(5,000,000 pixels). There are 5 video sequences in our testdatasets; the frames of our datasets are 1500, and the numberof vehicles in datasets is 3219. The test images include singlevehicle, multivehicle, and illumination changing in the scene.We use three indicators to measure the performance ofalgorithms: the detection rate 𝑟+, the false alarm rate 𝑟−,and the algorithm precision 𝑝+. The criterion to determine

a “good” detection in this paper is the overlap of the detectedbounding box versus the annotated bounding box. If theoverlap is larger than a certain threshold, the detection is a“good” detection. Consider

𝑟+ =Number of detected vehicles

Total number of vehicles in testing data set, (14)

𝑟− =Number of false alarms

Total number of vehicle ROI, (15)

𝑝+ = Number of detected vehicles

× (Number of detected vehicles

+ Number of false alarms)−1.

(16)

Experiment 1 (calculate the fuzzymeasure of each algorithm).In our multifeature fusion vehicle detection algorithm, fuzzymeasure of each feature-based algorithm is set accordingto the performance of its own. We test each feature-basedvehicle detection method on the same vehicle sample setnamed JVTL. The images in JVTV are vehicle ROIs detectedby shadow-based method which is introduced in Section 2.The positive samples of JVTL are vehicles, and the negativesamples are interferences in JVTL. The numbers of positiveand negative samples are 3219 and 6000. According to(16), the precision of each vehicle detection method canbe acquired. Let the precision 𝑝+ be the fuzzy measure 𝑔corresponding to eachmethod. According to Figure 9, we canset the fuzzy measure of every algorithm.

Experiment 2 (performance of ourmultifeature fusion vehicledetection algorithm). After setting fuzzy measure of eachfeature-based algorithm, we apply the sample set JVTL to testour method and every feature-based algorithm. As shown inFigure 10, the single feature cannot meet the requirement ofhigh detection rate and low false alarm rate. Our algorithmfuzzes the output of each single feature, and the result isdetermined by using the fuzzy judgment instead of directjudgment. At the same time, the use of fuzzy integral can givefull consideration to the cooperation of multifeatures and theimportance degree of each feature in the recognition phase.Therefore, our method outperforms each single feature. Inour experiment, the average processing time (AVT) of ourmethod can achieve 50ms per frame when processing onthe Caltech Rear public test images whose resolutions are896 × 592, which basically achieve real-time processing. Andthe processing time is 36ms per frame on images whoseresolutions are 640 × 480. Part of results of our algorithmare shown in Figures 11 and 12. Figure 11 is the result ofalgorithm on Caltech Rear public vehicle images; we set themain thresholds as follows: th BW = 0.1 and Th vehicle =0.9. Experimental results show that our method can detectwell vehicles in different distances.The distances are differentin Figures 12(a) and 12(b); the distances between vehiclesand camera are from 3m to 50m. Figure 12(c) shows thatour method can not only detect the single vehicle, but alsohandle themultivehicle detection problem. Figure 12(d) is thedetection result on urban road.


(a) (b) (c)

(d) (e)

Figure 11: Detection results on Caltech Rear public vehicle images.

(a)

(b) (c)

(d)

Figure 12: Detection results on our data set.

Experiment 3 (algorithm comparison). To verify the perfor-mance of our method, we compare our method to threefeature-based methods, the voting method of these threefeature-based methods, and vehicle detection methods in[11, 15, 16]. Algorithms used for comparison are all testedon the same collection (the public test library Caltech Cars(Rear) [27]).There are two ways to get the algorithms’ results.On one hand, we download the source code from thewebsites

which have been provided in their articles to get the testingresults. On the other hand, we directly use the testing resultsillustrated in the articles. Comparison result is shown inTable 2; it shows that the single feature-based methods candetect vehicle better, but the false alarm rate is also thehighest. Although the voting method can reduce the falsealarm rate, the detection rate is reduced either. Processingtime is another indicator to measure the performance of


Table 2: Algorithm comparison.

Methods Accuracy AVT (ms/frame)(DR/FAR) (896 × 592)

Wang and Lien [11] 98%/0% 510Li et al. [14] 98%/1% 500Ali and Shah [15] 90.2%/0.6% 500Taillight-based method 95.3%/23.4% 16Symmetry-based method 86.1%/48% 15HOG + AdaBoost 95.1%/44.8% 16Voting method 83.3%/0% 45Our method 95.5%/8.2% 50

algorithms; Ali andWang’s methods outperform our methodin terms of accuracy, but the processing time of theirmethodsis above 500ms. Considering both the accuracy and theprocessing time of algorithms, our method outperforms theother methods.

6. Conclusions

In this paper, we propose a multifeature fusion vehicledetection algorithm based on Choquet integral. There aretwo major contributions in this paper. First, we proposea taillight-based vehicle detection method, and a vehicletaillight feature similarity measure is defined. In addition, thevehicle symmetry and HOG + AdaBoost feature similaritymeasures are introduced combining with the definitionof Choquet integral. Second, these three feature similaritymeasures are fused by Choquet integral to detect vehiclesin both static test images and videos. In experiment part,our algorithm has been evaluated by using public collectionsand our own test images, and the experiment results areencouraging. But, to generalize our algorithm, there are stillseveral problems to solve, such as improving accuracy ofHOG + AdaBoost feature. To improve the performance ofvehicle detection methods, we will address these issues andimprove the multivehicle detection to an upper level.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgments

The authors would like to thank the reviewers and editors fortheir comments regarding enhancing the quality of the paper.This work is supported by Grants from Jilin Planned Projectsfor Science Technology Development (Grant no. 20120305and no. 20130522119JH) and Ph.D. Programs Foundation ofMinistry of Education of China (Grant no. 20130061110054).

References

[1] Z. Sun, G. Bebis, and R. Miller, “On-road vehicle detection:a review,” IEEE Transactions on Pattern Analysis and MachineIntelligence, vol. 28, no. 5, pp. 694–711, 2006.

[2] Y. M. Chan, S. S. Huang, L. C. Fu, P. Y. Hsiao, and M. F.Lo, “Vehicle detection and tracking under various lightingconditions using a particle filter,” IET Intelligent TransportSystems, vol. 6, no. 1, pp. 1–8, 2012.

[3] B. Lin, Y. Lin, L. Fu et al., “Integrating appearance and edgefeatures for sedan vehicle detection in the blind-spot area,” IEEETransactions on Intelligent Transportation Systems, vol. 13, no. 2,pp. 737–747, 2012.

[4] J. Hwang, K. Huh, and D. Lee, “Vision-based vehicle detectionand tracking algorithm design,”Optical Engineering, vol. 48, no.12, Article ID 127201, 2009.

[5] B. Southall, M. Bansal, and J. Eledath, “Real-time vehicledetection for highway driving,” in Proceeding of the IEEEComputer Society Conference on Computer Vision and PatternRecognition Workshops (CVPR '09), pp. 541–548, Miami, Fla,USA, June 2009.

[6] D. Y. Chen, G. R. Chen, and Y. W. Wang, “Real-time dynamicvehicle detection on resource-limited mobile platform,” IETComputer Vision, vol. 7, no. 2, pp. 81–89, 2013.

[7] Y. Tsai, K. Huang, C. Tsai, and L. Chen, “An exploration of on-road vehicle detection using hierarchical scaling schemes,” inProceedings of the 17th IEEE International Conference on ImageProcessing (ICIP ’10), pp. 3937–3940, Hong Kong, September2010.

[8] W. Chang and C. Cho, “Online boosting for vehicle detection,”IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, vol. 40, no. 3, pp. 892–902, 2010.

[9] S. Sivaraman and M. M. Trivedi, “A general active-learningframework for on-road vehicle recognition and tracking,” IEEETransactions on Intelligent Transportation Systems, vol. 11, no. 2,pp. 267–276, 2010.

[10] H. Tehrani Niknejad, A. Takeuchi, S. Mita, and D. McAllester,“On-road multivehicle tracking using deformable object modeland particle filter with improved likelihood estimation,” IEEETransactions on Intelligent Transportation Systems, vol. 13, no. 2,pp. 748–758, 2012.

[11] C. R. Wang and J. J. Lien, “Automatic vehicle detection usinglocal features—a statistical approach,” IEEE Transactions onIntelligent Transportation Systems, vol. 9, no. 1, pp. 83–96, 2008.

[12] D. Alonso, L. Salgado, and M. Nieto, “Robust vehicle detectionthrough multidimensional classification for on board videobased systems,” in Proceedings of the 14th IEEE InternationalConference on Image Processing (ICIP ’07), pp. IV321–IV324, SanAntonio, Tex, USA, September 2007.

[13] A. Jazayeri, H. Cai, J. Y. Zheng, and M. Tuceryan, “Vehicledetection and tracking in car video based on motion model,”IEEE Transactions on Intelligent Transportation Systems, vol. 12,no. 2, pp. 583–595, 2011.

[14] W. H. Li, H. Y. Ni, Y. Wang, B. Fu, P. X. Liu, and S. J. Wang,“Detection of partially occluded pedestrians by an enhancedcascade detector,” IET Intelligent Transport Systems, 2014.

[15] S. Ali and M. Shah, “A supervised learning framework forgeneric object detection in images,” in Proceedings of the IEEEConference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 1347–1354, San Diego, Calif, USA, June 2005.


[16] J. Gall and V. Lempitsky, “Class-specific hough forests for objectdetection,” in Proceedings of the IEEE Computer Society Con-ference on Computer Vision and Pattern Recognition Workshops(CVPR ’09), pp. 1022–1029, Miami, Fla, USA, June 2009.

[17] W. Sun, H. Gao, and O. Kaynak, “Adaptive backsteppingcontrol for active suspension systems with hard constraints,”IEEE/ASME Transactions on Mechatronics, vol. 18, no. 3, pp.1072–1079, 2013.

[18] W. Sun, Z. Zhao, andH. Gao, “Saturated adaptive robust controlfor active suspension systems,” IEEE Transactions on IndustrialElectronics, vol. 60, no. 9, pp. 3889–3896, 2013.

[19] W. H. Sun, H. J. Gao, and B. Yao, “Adaptive robust vibrationcontrol of full-car active suspensions with electrohydraulicactuators,” IEEE Transactions on Control Systems Technology,vol. 21, no. 6, pp. 2417–2422, 2013.

[20] W. Sun, H. Gao Sr., and O. Kaynak, “Finite frequency 𝐻∞con-

trol for vehicle active suspension systems,” IEEETransactions onControl Systems Technology, vol. 19, no. 2, pp. 416–422, 2011.

[21] G. D. Tian, M. C. Zhou, and J. W. Chu, “A chance constrainedprogramming approach to determine the optimal disassemblysequence,” IEEE Transactions on Automation Science and Engi-neering, vol. 10, no. 4, pp. 1004–1013, 2013.

[22] G. D. Tian, M. C. Zhou, J. W. Chu, and Y. M. Liu, “Probabilityevaluation models of product disassembly cost subject torandom removal time and different removal labor cost,” IEEETransactions on Automation Science and Engineering, vol. 9, no.2, pp. 288–295, 2012.

[23] G. Tian, J. Chu, Y. Liu, H. Ke, X. Zhao, and G. Xu, “Expectedenergy analysis for industrial process planning problem withfuzzy time parameters,” Computers and Chemical Engineering,vol. 35, no. 12, pp. 2905–2912, 2011.

[24] Z. Y. Wang and G. J. Klir, Fuzzy Measure Theory, Plenum Press,New York, NY, USA, 1992.

[25] Y.Wang andW. Li, “High-precision video flame detection algo-rithm based on multi-feature fusion,” Journal of Jilin University,vol. 40, no. 3, pp. 769–775, 2010.

[26] Y. Ding, W. H. Li, J. T. Fan, and H. M. Yang, “A moving objectdetection algorithmbase on choquet integrate,”Acta ElectronicaSinica, vol. 38, no. 2, pp. 263–268, 2010.

[27] “Caltech Cars (Rear),” http://www.vision.caltech.edu/html-files/archive.html.

[28] M. B. Qi, Y. Pan, and Y. X. Zhang, “Preceding moving vehicledetection based on shadow of chassis',” Journal of ElectronicMeasurement and Instrument, vol. 26, no. 1, pp. 54–59, 2012.

[29] Q. Zhu, S. Avidan, M. C. Ye, and K. T. Cheng, “Fast humandetection using a cascade ofHistograms ofOrientedGradients’,”in Proceedings of the IEEE Computer Society Conference onComputer Vision and Pattern Recognition (CVPR ’06), NewYork, NY, USA, 2006.

[30] K. Tieu and P. Viola, “Boosting image retrieval,” in Proceedingsof IEEE Conference on Computer Vision and Pattern Recognition(CVPR ’00), pp. 228–235, Hilton Head Island, SC, USA, June2000.

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Research Article Multifeature Fusion Vehicle Detection ...integral, the vehicle symmetry similarity measure and the HOG + A daBoost feature similarity measure are de ned. Finally,