Abstract
Video detection is a valuable application in intelligence transportation systems. Some common traffic flow parameters like volume, velocity and vehicle category could be estimated automatically by video detection in real-time. At present, most of Video detection systems focus on algorithms designed and applied for day-time conditions. In this paper, a visual-based vehicle detection method for nighttime conditions is developed. Firstly, we consider there were two kinds of traffic scenes at nighttime: lamp scene and non-lamp scene. We calculate the illumination visibility of the region of interest (ROI) background image to divide traffic scenes into two predefined categories based on Support Vector Machine (SVM). Then, Different algorithms are used to complete the vehicle detection for two night scenes. At last, we test our approach with several video sequences from realistic traffic scenes including lamp scenes and non-lamp scenes. Experimental results show good performance of vehicle detection for nighttime conditions.
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