Illumination Assessment for Vision-Based Traffic Monitoring SHRUTHI KOMAL GUDIPATI

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1 Illumination Assessment for Vision-Based Traffic Monitoring By SHRUTHI KOMAL GUDIPATI

2 Outline Introduction PVS system design & concepts Assessing lighting Assessing contrast Assessing shadow presence Conclusion

3 Introduction Vision systems in traffic domain operates autonomously over varying environmental conditions Uses different parameter values or algorithms depending on these conditions Parameters depends on ambient conditions on camera images

4 PVS system Commercial real-time vision system for traffic monitoring that detects, tracks, and counts vehicles Uses large volume of video data obtained from 25 different scenes Switches between different parameter values and algorithms depending on scene illumination aspects

5 Aspects of Scene illumination Is the scene well-lit? Is vehicle bodies visible? In poorly lit scenes, Are only vehicle lights visible?

6 Aspects of Scene illumination

7 Aspects of Scene illumination

8 Aspects of Scene Iillumination Is the contrast sharp enough? Ex: Is visibility sufficient for reliable detection? Is visibility sufficient or too diminished? Fog, Dust or Snow

9 Aspects of Scene illumination Are vehicles in the scene casting shadows?

10 PVS System Design Processes frames at 30 Hz Process images simultaneously up to 4 cameras Compact and fits in 3U VME board

11 3U VME Board

12 PVS system hardware Two Texas Instruments TMS320C31 DSP chips A Sensar pyramid chip Custom ALU implemented using a Xilinx chip

13 Operation principle Maintains a reference image that contains the scene as it would appear if no vehicles were present Each incoming frame is compared to the reference Pixels where there are significant differences are grouped together into "fragments" by the detection algorithm These fragments are grouped and tracked from frame to frame using a predictive filter

15 One dimensional strip representation Reduces the 2D image of each lane to a 1D "strip Integration operation that sums two pixelwise measures across the portion of each image row that is spanned by the lane, resulting in a brightness and energy measurement for each row Integration operation is performed by the ALU, which takes as input a bit-mask

16 2D -> 1D transformation

17 Strip measurements Two measurements, brightness and energy, are computed for each strip element y of each strip s Brightness B(s,y) = Σ (pixels inwy) Energy E(s,y) = [Σ (absolute difference between every two ااWyاا / ] W) adjacent pixels in

18 Reference strips Brightness and Energy measurements gathered from a strip over time are used to construct a reference strip For scenes in which traffic is flowing freely, reference strip can be constructed by IIR filtering IIR filtering doesn t work in stop-and-go or very crowded areas

19 Reference strips Strip element in reference image is updated If 1 second has elapsed after the last time a significant dt (frame-to-frame difference) value was observed at that position If 1 minute has elapsed since the last update

20 Strip element classification Classify each strip element on the current strip as background or nonbackground Done by computing the brightness and energy difference measures ) اا W(y) اا ΔB(y) = B(I,y) - B (R,y) - (o اE(R,y (E(I,y) - ا = ΔE(y)

21 Classification as Background or non- Background

22 Strip element classification Each strip element that is classified as non-background is further classified as "bright" or "dark Depends on whether its brightness is greater or less than the brightness of the corresponding reference strip element

23 Illumination Assessment All frames grabbed in a two-minute interval, all strip elements that both have been identified as non- background and have significant dt are used to update various statistical measures Values of these measures are used to assess the lighting, contrast, and shadows

24 Fragment Detection Groups non-background strip elements into symbolic "vehicle fragments To prevent false positive vehicle detections, the system avoids detecting the illumination artifacts as vehicle fragments

25 Fragment Detection Uses three different detection techniques, depending on the nature of the scene illumination Detection in well-lit scenes without vehicle shadows Detection in well-lit scenes with vehicle shadows Detection in poorly-lit scenes

26 Fragment Detection Detection in well-lit scenes without vehicle shadows Scene as well-lit if the entire vehicle body is Visible Scenes are termed poorly-lit if the only clearlyvisible vehicle components are the headlights or taillights

27 Fragment Detection Detection in well-lit scenes with vehicle shadows Well-lit scenes where vehicles are casting shadows, the detection process must be modified so that non-background strip elements due to shadows are not grouped into vehicle fragments Uses stereo or motion cues to infer height

28 Fragment Detection Detection in poorly-lit scenes Where only vehicle lights are visible, fragment extraction via connected components is prone to false positives due to headlight reflections Fragments are extracted by identifying compact bright regions of non-background strip elements around local brightness maxima

29 Fragment tracking & grouping After the vehicle fragments have been extracted, they are passed to the Tracker module which tracks over time and groups them into objects

30 Assessing lighting Measures used for assessing whether the scene is well-lit, i.e. whether the entire body of most vehicles will be visible Ndark + Nbright = total number of nonbackground pixels that were detected Pdark = Ndark/(Ndark+Nbright) If the scene is poorly-lit, the background image will be quite dark, and it will be difficult to detect any pixel with a dark surface color. Under this condition ndark will be small, and hence Pdark will be small

32 Assessing contrast Two typical causes of insufficient contrast -- fog or raindrops Contrast can be measured using the energy difference measure ΔE(y) In low-contrast scenes that occur during the day, vehicles will usually appear as objects darker than the haze, which often appears rather bright In low-contrast scenes occurring at night, no dark regions will be detectable Measure ΔEbright and ΔEdark

34 Assessing shadow presence Scenes that are well-lit can be decomposed into two sub-classes Shadows Non - Shadows Contrast of a "bright" portion of a vehicle against the road surface would be less than that of a "dark" portion

36 Assessing shadow presence Using k4 = 1.2, this method has been found to work well Sometimes, when there are very faint shadows, it does classify the scene as having no shadows Fails when the background is not a road For example, in some scenes a camera is looking at the road primarily from the side, and the vehicles occlude either objects (e.g. trees) or the sky as they move across the scene

37 Illumination Assessment module Three methods for assessing lighting, contrast, and shadows are applied sequentially

38 Illumination Assessment module

39 Conclusions During Strip representation, transformation from 2D -> 1D is not clearly explained In strip classification, the global offset o is mentioned to have been measured by a different process. The paper doesn t mention/explain anything about the process The paper mentions that the deployment results were satisfactory but it doesn t provide any statistical data to support the claim

40 References Wixson, L.B., Hanna, K., Mishra, D., Improved Illumination Assessment for Vision-Based Traffic Monitoring, VS98(Image Processing for Visual Surveillance) Hanna, K. L. Wixso and D. Mishra, Illumination Assessment for Vision-Based Traffic Monitoring, ICPR '96: Proceedings of the International Conference on Pattern Recognition Femer et al. 941 N.J. Ferrier, S.M. Rowe, A. Blake, "Real-Time Traffic Monitoring," in Proceedings of the IEEE Workshop on Applications of Computer Vision, pages 81-88, 1994 Kilger 911 M. Kilger, "A Shadow Handler in a Video-based Realtime Traffic Monitoring System", in Proceedings of the IEEE Workshop on Applications of Computer Vision, pages 11-18, 1992

41 Questions?

Shadows. COMP 575/770 Spring 2013

Shadows. COMP 575/770 Spring 2013 Shadows COMP 575/770 Spring 2013 Shadows in Ray Tracing Shadows are important for realism Basic idea: figure out whether a point on an object is illuminated by a light source Easy for ray tracers Just