PEDESTRIAN RE-IDENTIFICATION USING COLOR FEATURE IN MULTI SURVEILLANCE VIDEO

In this paper we present a system to solve the problem of moving pedestrian re-identification in surveillance video. Surveillance video has low-resolution, high video noise and limited monitoring scope. Our proposed framework must deal with several problems such as variations of illumination conditions, poses and occlusions. How to extract the robust feature that can adapt the problems have been the task. The people of global color approaches do not change in the process of monitoring. Our paper use the color histogram as feature descriptors and choose RGB HSV and UVW for color space. Traditional histogram method extract the global color approach as the feature. The object color structure information will be neglected. We use the SPM model supplement the structure information for the histogram. The results of a test from a real surveillance system show that our method can provide a probability of matching

A framework for track matching across disjoint cameras using robust shape and appearance features

This paper presents a framework based on robust shape and appearance features for matching the various tracks generated by a single individual moving within a surveillance system. Each track is first automatically analysed in order to detect and remove the frames affected by large segmentation errors and drastic changes in illumination. The object’s features computed over the remaining frames prove more robust and capable of supporting correct matching of tracks even in the case of significantly disjointed camera views. The shape and appearance features used include a height estimate as well as illumination-tolerant colour representation of the individual’s global colours and the colours of the upper and lower portions of clothing. The results of a test from a real surveillance system show that the combination of these four features can provide a probability of matching as high as 91 percent with 5 percent probability of false alarms under views which have significantly differing illumination levels and suffer from significant segmentation errors in as many as 1 in 4 frames.C. Madden and M. Piccardihttp://doi.ieeecomputersociety.org/10.1109/AVSS.2007.442530