Real-time Human Body Detection And Tracking Based On Head Feature Extraction And Its Application

The detection and tracking of human body is an important part of the visual analysis of human movement. It has a good prospect of application and potential economic value in video conference, medical diagnosis, interaction between man and machine, intelligent video monitoring, virtual reality, image storage and retrieval based on content and so on. In some application fields of the detection and tracking of human body, such as intelligent monitoring and passenger flow detection, only the vertical view images can be captured because of the restricted position of image capture device or to avoid occlusion among the human body as much as possible. In this kind of image, only the head, top of head in particular, will be shown completely among all of the human body parts and the information of heads in vertical view image, such as the contour of top of head and the color distribution in the head region and so on, is the only feature to distinguish the multiple human bodies from complicated circumstance. So the selection and extraction of head feature is the key of the detection and tracking of human body in vertical view image. Centering about the problem of the detection and tracking of human body in vertical view image and complicated circumstances, this dissertation presents a method to detect and track local human body based on head feature extraction and the global human body's motion will be estimated by using the detection and tracking of local human body. To obtain the head feature in a real-time low-end embedded platform, the head's contour feature extraction method based on modified Hough Transform and the bead's depth and perspective feature extraction method based on target disparity acquirement are presented. And these two methods are used to realize real-time vision-based human body detection and tracking system with higher demanding of accurate rate.To extract the head's contour feature in vertical view image exactly with lower complexity algorithm and avoid false and leak recognition as much as possible, this dissertation adopts GHT as the main technique to extract the head's quasi-circle contour. At the same time, to further lower the time consume of GHT and to assure the head contour curve's best fitting circle contour can be extracted, further modifications are introduced in the accumulation process in parameter space and the validation process of candidate circle on the basis of preserving the GHT's mapping principle in parameter space, i.e. the TGHT algorithm which aims at head's quasi-circle contour detection with larger distortion and the best fitting contour extraction method based on theory of perceptive grouping are presented. The TGHT's performance test and the experiment results show that the head's contour feature extraction method based on TGHT takes into account both the accuracy and the real-time performance and it can be applied in the field of real-time vision-based detection and tracking of human body with higher demanding of accurate rate.To solve the problem that head recognition method based on head's contour feature extraction can not distinguish true head regions from many false regions similar to true head region, this dissertation obtains the stereo disparity of candidate head region by using target disparity method on the basis of bead's contour feature extraction and the candidate head regions are validated to improve the accurate rate of head recognition by using the head's 3D feature composed of the correspondence between head's depth and disparity and the perspective relationship between heM's disparity and scale. The experiment results show that the head recognition method based on bead's depth and perspective feature extraction has higher accurate rate than the method based on head's contour feature extraction because most false head regions have been eliminated by using the head's 3D feature.After head recognition, to perform the detection and tracking of human body in vertical view image sequence by using the motion detection and local human body tracking based on head feature extraction, this dissertation presents two methods, i.e. the edge background subtraction method to eliminate the background edges which are out of the moving human body and the head tracking method based on Kalman prediction and head's contour feature matching between adjacent frames.At the end of this dissertation, according to different head feature extraction methods, two kinds of embedded vision-based human body detection and tracking system based on low-end DSP are introduced. The field test results of the vision-based human body detection and tracking system based on head's contour feature extraction applied to bus passenger flow detection and the comparison results of two kinds of vision-based human body detection and tracking method applied to the same simulation sequence image are both presented.