Change Detection and Background Update Through Statistic Segmentation for Traffic Monitoring

Abstract

Recent advances in computer imaging have led to the emergence of video-based surveillance as a monitoring solution in Intelligent Transportation Systems (ITS). The deployment of CCTV infrastructure in highway scenes facilitates the evaluation of traffic conditions. However, the majority of video-based ITS are restricted to manual assessment and lack the ability to support automatic event notification. This is due to the fact that, the effective operation of intelligent traffic management relies strongly on the performance of an image processing front end, which performs change detection and background update. Each one of these tasks needs to cope with specific challenges. Change detection is required to perform the effective isolation of content changes from noise-level fluctuations, while background update needs to adapt to timevarying lighting variations, without incorporating stationary occlusions to the background. This paper presents the operation principle of a video-based ITS front end. A block-based statistic segmentation method for feature extraction in highway scenes is analyzed. The presented segmentation algorithm focuses on the estimation of the noise model. The extracted noise model is utilized in change detection in order to separate content changes from noise fluctuations. Additionally, a statistic background estimation method, which adapts to gradual illumination variations, is presented.