University of Washington

Technology Transfer (cont'd) US 8,358,808 B2 VIDEO-BASED VEHICLE DETECTION AND TRACKING USING SPATIO-TEMPORAL MAPS Through a funded research project by the Region 10 UTC, a video-based traffic data collection method and systems were developed by Yegor Maloknovskiy, Yinhai Wang, and Yao-Jan Wu for detecting and tracking objects, such as motor vehicles, within video data. The systems and method analyze video data, for example, to count objects, determine object speeds, and track the path of objects without relying on the detection and identification of background data within the captured video data. The methodology is very efficient because the detection uses one or more scan lines to generate a spatio-temporal map and then extract traffic data from the map. A spatio-temporal map is a time progression of a slice of video data representing a history of pixel data corresponding to a scan line. The detection system detects objects in the video data based on intersections of lines within the spatio-temporal map. Once the detection system has detected an object, the detection system may record 32 Pacific Northwest Transportation Consortium the detection for counting purposes, display an indication of the object in association with the video data, determine the speed of the object, etc. The proposed algorithm was implemented in Microsoft Visual C++ using OpenCV and Boost C++ graph libraries. Six test video data sets, representing a variety of lighting, flow level, and camera vibration conditions, were used to evaluate the performance of the new algorithm. Experimental results showed that environmental factors do not significantly impact the detection accuracy of the algorithm. This patent was published in January 2013. The figure below shows the spatio-temporal map extracted for various challenging detection scenarios. For questions, please contact Dr. Yinhai Wang (yinhai@uw.edu, 206-616-2696)

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