Object Detection Algorithm in Traffic Video Surveillance Based on Compressed Sensing

摘要

To adapt the contradiction between the increasing information quantity of highway traffic network monitoring and the limited network bandwidth resources, this paper proposes an object detection algorithm based on Bayesian compressed sensing. Video are sparse in a wavelet base, and a partial Hadamard measurement matrix is adopted to compress the video. The object detection method combines background difference and Bayesian compressed sensing of wavelet tree structure. To get more accurate foreground, an adaptive background model is proposed. Experiments results show the accuracy and effectiveness of the method, and can robustly detect the targets under changing light and reduce the price of video transmission.