Object detection based on moving edges

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Intrusion Detection Using Extraction of Moving Edges *A.Makarov, J .-M. Vesin and M.Kunt

Signal Processing LaboratorySwiss Federal Institute of TechnologyLausanne, CH 1015

AbstractIn this article we present an image processing ba-

sed method for intrusion detection. The algorithmi scharacterizedby a low computational cost, a high sen-sitivity to the presence of objects, robustness to illu-mnation changes and modest memory requirements.The suggested approach i s to separate the moving obj-ect edges from the background.

1 I ntroductionImage processing offers the possibility to integrate

surveillance of restricted areas and coding (compres-sion and transmission) of frames containing intrusions.The intrusion detection algorithms should satisfy a setof criteria that may seem contradictory:1.2.3.4.5.

High sensitivity to the presence of intruding obj-ects.Robustness and resilience to slow or fast illumi-nation changes.Independence from the configuration of thebackground-scene.Conceptual independence from the frame-rateofthe input signal.Speed allowinga reliably frequent checkup. Inpractical applications, this means that their com-putational cost should be minimized.

Existing algorithms use different principles to appro-ach thisproblem. Thestatistical segmentation appro-ach [l]may give good results, but iscomputationallyexpensive. Methods based on adaptive extraction [2]of background are suitable for keeping track of slowchanges of illumination] but their efficiencyisimpairedin the presence of abrupt changes [3],which often hap-pen in indoor scenes (dueto reflections, shadows etc.).

*Thiswork was supportedby CERS grant 2456. 1

Lighting changes are the main cause of false alarmsin image based intrusion detection. Edges are muchmore robust against lighting changes than luminance.An approach based on edge extraction was proposedby Bartolini and Cappellini [3] to design an intrusiondetector dealing successfully with variations of light.Robustness of the algorithm is enhanced by selectionof reference background contour segments using theHough Transform. This property isachieved howeverat the expense of sensitivity, for the method becomesdependent on the location of the reference straight-lineedges. The computational and memory costs imposedby the use of the two-dimensional Hough Transformmay be critical for low-cost implementations.Our work conserves the edge-extracting approach]but tries to remedy the aforementioned drawbacks. Inorder to avoid the scene dependence, we base our de-tection on suppression of background edges and en-hancement of moving edges, i.e. edges of the mo-ving object. Beside robustness and sensitivity, themost important criterion to which we submittedwasanon-expensive and efficient hardware implementationviability.

Our algorithm is presented in full detail in this pa-per. In section 2 we present the extraction of movingedges. In section 3 we show our results compared tothe Hough Transform based method [3]. Finally, theconclusion is given in section 4.

2 Extraction of Moving EdgesThe goal of this algorithm is to extract the edges of

the moving object and to suppressthe edges containedin the background. The intrusion detection may thenbe performed by comparing the number of points inthe extracted contours with a fixed threshold.As many other change detection algorithms, wesimplify the problem by processing images issued froma fixed camera. The current input image is compa-red with the stored reference. If intrusion is detected,the imageis transmitted. If the number of extracted

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other. The resulting bi-level image will consist ofthe full gradient edges of the moving object (ifany), the sparsed remains of the backgroundgra-dient edges, and of more noise than before.

3. A simple median fiilteringof window size 3x3 isperformed in order to conserve the moving edgesand to remove the background remains and noise.This is possible because the only dense regionsin the binary difference image correspond to theedges of the moving object.

4. The above steps may suffice to detect the intru-sion. Anyway, to make sure that the undesirableremains of the background edges and noise areremoved, we extract the outline of the medianfiltered edges. The resulting contour should en-velop the moving olbject. The outline points areobtained by taking the first and the last points ineach row and each column. These points are thensequentially ordered. If some point or some small

Figure 1: the block-scheme of the intrusion detectorpoints is below the threshold, the reference image isupdated.We shall describe now each individual step of thealgorithm of Figure 1.1. For the sake of simplicity and robustness we do

not pay much attention to the precision of kxtra-cted contours. This allowsus to use the fastestway to extract the edges: the gradient. As a mat-ter of fact our edge extraction consists of simplesubtraction of consecutive rows and columns, andaddition of the absolute values of thus obtainedhorizontal and vertical components. That is whywe called this simplified procedure pseudogra-dient.Further simplification of gradient images is doneas usually by thresholding. We used a low fixedthreshold which produced bi-level images conta-ining large (i.e. badly localised) edges andahighamount of noise.If the input image represents the background, thebi-level gradient image is stored in memory as thereference. This reduces the amount of memoryneeded to store a reference background

2. An AND logical operation isperformed on thecurrent bilevel gradient image and the comple-ment of the reference gradient image input edges.Hence, if an edge-point appears in the referencebackground image, and not in the current inputimage, it will be simply ignored. The backgro-und edge-points that coincide will annihilate each

group of points isfar away from the previous andthe succeeding points, it isremoved.

5 . In the basic algorithm of Figure 1,the remainingedge-points are counted up and their sum corn-pared to a fixed threshold. In [4], it was chosento detect large increases in the number of edge-points in order to enhance the robustness whendealing with changing contrastsor very noisy ima-ges.

Step 2 was used in a recent approach developed inde-pendently by Aranda et al. [5].

3 ResultsSteps 1-5 of the described intrusion detection al-

gorithm are applied to a 64 frame sequence whichrepresents a human being trespassing a watched onarea. Dimensionsof images are 288x353 pels, lumi-nance range is 0-255. The frame numbers 0 and 64represent the reference images. The human enters thescene in the first frame and exits in the 63rd frame.Figure 3 shows the number of extracted moving-edgepoints for each frame.

The method we used to test the robustness of thealgorithm to illumination changes consists in adding(subtracting) a fixed amount of luminance to (from)each pixel, which gives brightening (darkening) effect[3]. The increment we used to generate brightenedand darkened sequence is 50 grey-levels. It is largerthan the one suggested in [3],which allowed US toolb-serve the behaviour of the algorithm in the presence

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Figure 2: Moving edges detection in normal illumina-tion conditions.

Figure 3: The signals extracted from brightenedand darkened sequences deviate insignificantly withrespect to the moving edge points sum of the originalsequence.

of saturation due to limited range of luminance. InFigure3, the number of moving edge-points extractedfrom brightened and darkened sequences issubtractedand divided by the corresponding number for the ori-ginal sequence. Due to saturation, some edges disap-pear, so the obtained values are negative. These va-riations are very low, hence the algorithm is robustwith respect to the applied lighting change model. Inall cases,high sensitivity to intrusion is preserved, andthe extracted signal is zero when no intruding objectoccurs. For more complex illumination change mo-dels, the change detection method described in [4] ispreferred to the comparison with the fixed threshold.

Figures 4, 5 and 6 represent the bi-level imagesatdifferent stages of the algorithm.For sake of comparison, weshow on Figures 7,8 and9 the results obtained by the reference edges method

0

Figure 4: Reference Background Pseudogradient.

Figure 5: Pseudogradient of the current input image.

0,

Figure 6: Extracted outline.

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Figure 7: Reference edges detection in the originalsequence.

Figure 8: Reference edges detection in the darkenedsequence.

[3]. Due to the spatial distribution of the referenceedges, this method turns out to be less sensitive. Itsrobustness is impaired by extreme illumination va-riations. For the tested population of images, ourmethod detected intrusion in 97% of frames versus49% for the Hough Transform based method.

4 ConclusionEdge-extraction may be used in intrusion-detection

methods in order to enhance their robustness againstillumination changes. In this paper we presented analgorithm based on edge-extraction that is characteri-sed by high robustness to illumination changes as wellas high sensitivity to the presence of physical obje-cts. Low computational cost and storage requirementsare additional assets of this intrusion detector. Themethod is flexible: if some modules are omitted thedetection is simpler and faster at the expense of ro-

0.961 i

Figure9: Reference edges detection in the brightenedsequence.

bustness [4]. Addition of new modules turn i t intoa preprocessor for object recognition or classificationapplications.The decision about the existence of intrusion wasmade here by comparin,g the extracted signal with afixed threshold. This is justified by the fact that nomoving edge point isextracted when no intrusion oc-

curs, which holds for the illumination change modelwe used. In [4], we achieved robustness to variouscontrasts and high signetl-tcmoise ratios by detectingchanges in the extracted signal.

References[l] P.Bouthemy and P.Lalande. Motion detection in animagesequence using Gibbs distributions. Proceedings

ICASSP 89, 3:1651-1654, May 1989.[2] Klaus-Peter Karmann, Achimvon Brandt, and RainerGerl. Moving object segmentation based on adaptivereference images. Signal Processing V: Theories and

Applications,pages 95:l-954, 1990.Automatic detection

of intrusions by image processing. Proceedings of theInternational Conference on Digital Signal Processing,

Change detection using Joint NLOS.Technical Report 94-07, Signal Processing Labora-tory, Swiss Federal Instituteof Technology, Lausanne,Switzerland, August 1!>94.[5] C. Le6n J . Aranda and M. Frigola. A multitrackingsystem for trajectory analysis of people ina restricted

area. I n V. Cappellini, editor, Proceedings of the 4thInternational Workshop on Time- Varying Image Pro-cessing and Moving Object Recognition,number 3, pit-ScienceB.V., Amsterdam, 1994.

[3] F.Bartollini and V.Caqpellini.

2:468-471, July 1993.[4] A. Makarov.

ges 118- 124, Florence, I taly, J une 10-11 1993. Elsevier

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Object detection based on moving edges