Study On Multi-pedestrian Detection And Tracking Technology With Monocular Vision

Pedestrian detection is an important research area with many applications such as intelligent video surveillance, intelligent vehicle /driver assistance, motion analysis, and advanced human-machine interface, which has become the frontier and hot topic in the domain of computer vision in recent years. Pedestrian detection technology includes the pedestrian object detection, extraction, recognition, tracking and other aspects, and has some difficulties and challenges in occlusion, large variations of clothing, pose change, the arbitrariness and randomness of human movement, as well as the difference and complexity of application environment, which make it a difficult and challenging task and has received much attention from researchers.To avoid the occlusion among human body as much as possible, the pedestrian image of detection area was captured with vertical monocular camera and a method of detecting and tracking the multi-pedestrian based on head feature extraction was presented. Centering about the problem of multi-head detection and tracking under variation of formation and scale, the dissertation has made an in-depth study and discussion on head segmentation in static image, head identification based on multi-feature, fast detection of multi-head target in color image sequence, head tracking adapting to the change of object scale in complex scenes, thus the automatic detection and tracking methods of multi-pedestrian in image sequences was formed.The main study contents are summarized as follows:①The dissertation described the background, development and significance of pedestrian detection technology, based on the comprehensive comparison of various means of information perception, it focused on the status and research progress of computer vision-based pedestrian detection technology, and the drawbacks of current pedestrian detection technology were also discussed.②The dissertation made a detailed analysis of pedestrian information collection and the characteristics of pedestrian images with different collection way. Under the condition of serious occlusion among the human bodies, the full-body based and part-assembling based method was difficult to implement multi-pedestrian detection, and the head features were adopted to distinguish the multiple human bodies from complicated circumstances. Based on the analysis of the head features and the existed method of head detection under the vertical monocular vision, a novel method of head detection is presented by integrating multiple features such as color, contour and motion et al.③The dissertation studied on the segmentation and recognition technology of multi-head target in single color image.To solve the problem that the segmentation algorithm adopting the hair color or grayscale information was sensitivity to illumination and could not segment the head regions perfectly, an improved mean shift algorithm for color image segmentation was presented to extract head candidate regions. Compared with the conventional mean shift algorithm, the algorithm made improvements on two aspects: one is the adaptive bandwidth computing method on the ground of correlation comparison, which based on the similarity change relation between the original image and the segmented image with different bandwidth, the other is the better smoothness of the segmented image, which took the effect of pixels'color information and spatial information into account and took the median value of kernel window as the value of the convergence point.Furthermore, considering the trade-off between expeditiousness and effectiveness, the cascaded detector based on the contour information and inside color information of candidate head components was generated to implement the recognition of head. Experiment shows that the method can effectively eliminate fake regions whose color information is similar to hair color distribution or whose contour is quasi-circle, and the accuracy of head target identification is improved.④A method combination of motion segmentation and hair color partition was presented to implement multi-head target detection in color image sequence. On the basis of studying prior work, a algorithm based on three-frame-differencing and color edge information was proposed to perform the detection of moving object regions, and implement the extraction and location by using hair color segmentation and analyzing the connected component characteristics of candidates.⑤On the basis of analyzing the head motion characteristics of deformation and rotation, the mean shift method was introduced into pedestrian head tracking and gave out the improvement aiming at its drawback. Firstly, the algorithm took the LTP texture as the key feature for head tracking, and the target model was represented by fusioning LTP texture cue and color cue. Secondly, aiming at pedestrian tracking in large motion area, the selection method of initial point for mean shift tracking was proposed by combining motion direction information and kernel matching based on Bhattacharyya coefficients. Finally, the adaptive tracking window based on the principal components analysis was adopted and the pedestrian tracking method based on mean shift was formed, which could adapt to the change of object scale in complex scenes.⑥Based on the study mentioned above, the method integrating the technology of head detection, tracking and matching was proposed, and employed to implement the automatic multi-pedestrian detection and tracking with variable number, which was applied to the pedestrian trace tracking in video surveillance and passenger counting at bus entrance. The experiment show that the proposed method can solve the problem of the emergence of new object,temporal disappearance of object,false negative and false alarm of object, and make the detection result more accurate and reliable.The experiments of individual detection and tracking algorithms, as well as the integrated application in specific field indicate that the proposed algorithm improves the accuracy of pedestrian detection and tracking under the vertical monocular vision, and it lays the technology foundation for multi-pedestrian detection in complicated scenes.