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Shadow Classification for Detecting Vehicles in a Single Frame  

Lee, Dae-Ho (경희대학교 교양학부)
Park, Young-Tae (경희대학교 전자정보대학)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.11, 2007 , pp. 991-1000 More about this Journal
Abstract
A new robust approach to detect vehicles in a single frame of traffic scenes is presented. The method is based on the multi-level shadow classification, which has been shown to have the capability of extracting correct shadow shapes regardless of the operating conditions. The rationale of this classification is supported by the fact that shadow regions underneath vehicles usually exhibit darker gray level regardless of the vehicle brightness and illuminating conditions. Classified shadows provide string clues on the presence of vehicles. Unlike other schemes, neither background nor temporal information is utilized; thereby the performance is robust to the abrupt change of weather and the traffic congestion. By a simple evidential reasoning, the shadow evidences are combined with bright evidences to locate correct position of vehicles. Experimental results show the missing rate ranges form 0.9% to 7.2%, while the false alarm rate is below 4% for six traffic scenes sets under different operating conditions. The processing speed for more than 70 frames per second could be obtained for nominal image size, which makes the real-time implementation of measuring the traffic parameters possible.
Keywords
Shadow classification; Traffic information processing; Vehicle detection; Evidential reasoning;
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