Research And Implementation Of Non-overlapping Multi-Camera Pedestrian Re-identification

Recently,with the development of smart city and the internet of things,video surveilance network has been widely used in various industries.Person re-identification is a very challenging problem and has a practical application value.It plays an important role in video surveillance systems because it can reduce human efforts in searching for a target from a large number of videos.This topic has gained increasing interests in computer vision recently.Nowadays,person re-identification algorithms have been applied in criminal investigation,where the interference of passers-by can be eliminated to help the police find final suspects.However,differences in color,illumination,posture,imaging quality,as well as low-resolution of the captured frames cause large appearance variance across multiple cameras;thus,person re-identification remains a significant problem.In order to improve the accuracy of person re-identification,we propose an algorithm for person re-identification which based on multi-feature fusion and alternating direction method of multipliers.The main work and innovation of this thesis are follows:1.The original images are processed by the image enhancement algorithm to reduce the impact of illumination changes.This enhancement algorithm is committed to provide an image that is close to human visual characteristics.Then,the method of non-uniform segmentation that processes images is used.The method uses a sub-window size of 10-by-10 pixels with 5-pixel overlapping steps to obtain the local information of the pedestrian image.Meanwhile,the method uses the specific region mean method to divide the pedestrian image into five blocks.Specifically,depending on the difference of the expression ability of the legs and torso,these parts are divided into three blocks and two blocks,respectively.Then,the second and third blocks take the maximum operation,whereas the other blocks perform the mean operation because the second and third blocks are less affected by ambient noise compared with the other blocks.We also extract the HSV and LAB color features of the processed images,a texture feature of scale-invariant local ternary pattern and a shape feature of histogram of oriented gradient.2.The existing pedestrian re-identification algorithms generally consider the matching between local regions to eliminate the gap information between blocks.The combination of the global and local methods can effectively solve this problem.The proposed algorithm uses the multi-feature fusion method to combine the global and local information,which combines the global and local similarity measurement function of the related person,to obtain the final similarity function.Finally,the optimal distance measurement matrix is updated by the alternating direction method of multipliers,and the final similarities between each pair are obtained to conduct the re-identification.3.Based on above work and innovations,a simple multi-camera person re-identification system is designed to achieve person re-identification and target tracking.