Illumination Assessment for Vision-Based Traffic Monitoring By SHRUTHI KOMAL GUDIPATI
Outline Introduction PVS system design & concepts Assessing lighting Assessing contrast Assessing shadow presence Conclusion
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
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
Aspects of Scene illumination Is the scene well-lit? Is vehicle bodies visible? In poorly lit scenes, Are only vehicle lights visible ?
Aspects of Scene illumination
Aspects of Scene illumination
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
Aspects of Scene illumination Are vehicles in the scene casting shadows?
PVS System Design Processes frames at 30 Hz Process images simultaneously up to 4 cameras Compact and fits in 3U VME board
3U VME Board
PVS system hardware Two Texas Instruments TMS320C31 DSP chips A Sensar pyramid chip Custom ALU implemented using a Xilinx chip
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
One dimensional strip representation Reduces the 2D image of each lane to a 1D "strip“ Integration operation that sums two pixel- wise 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 identifying each lane
2D -> 1D transformation
Strip measurements Two measurements, brightness and energy, are computed for each strip element y of each strip s Brightness B(s,y) = Σ (pixels in Wy) Energy E(s,y) = [Σ (absolute difference between every two adjacent pixels in W) ] / ااWyاا
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
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
Strip element classification Classify each strip element on the current strip as background or non- background Done by computing the brightness and energy difference measures ΔB(y) = B(I,y) - B (R,y) - (o اا W(y) اا ) ΔE(y) = ا (E(I,y) - E(R,y ا
Classification as Background or non- Background
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
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
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
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
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 clearly- visible vehicle components are the headlights or taillights
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
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
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
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 non- background 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
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
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
Assessing shadow presence Using k 4 = 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
Illumination Assessment module Three methods for assessing lighting, contrast, and shadows are applied sequentially
Illumination Assessment module
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
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 Real- time Traffic Monitoring System", in Proceedings of the IEEE Workshop on Applications of Computer Vision, pages 11-18, 1992
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