Research On Target Relay Tracking Of Multi-Camera In Large Complex Scene

In the natural complex environment, it is difficult to extract the target features and track the target because of the interference factors of the background. Meanwhile, because the resolution and ken of the camera are limited, the applications of single camera tracking system are restricted, and the applications of multi-camera tracking system become inevitable. So the moving target relay tracking based on the multi-camera in large complex scene is a focal point of research in computer vision. The research results can be widely applied in vision autonomous aviation robots, cooperation of multi-agent, suspicious targets successively tracking, intelligence traffic surveillance and vehicles peccancy tracking, and pilotless aircraft and vehicles systems in military fields. Therefore the research results possess significant theoretical value and practical value, and have wide development and application foreground.This thesis mainly aims to study the target relay tracking of multi-camera in large complex scene. Firstly, the target tracking algorithm of single camera in the limited complex scene is further studied. And the research results can realize the moving target tracking in the complex scene. Then, the active tracking algorithm based on the single PTZ (pan-tilt-zoom) camera is proposed which can keep the target in the center area of the camera scene. Finally, the preliminary study about the target relay tracking theories and algorithms by multi-camera in the wide scene is performed. The major contributions of this thesis are summarized as follows:In the moving target tracking part, the tracking algorithm based on mean shift is studied, which uses three-dimensional background-weighted color histogram to characterize the target. This algorithm has good adaptability to target round, transmogrification, and part-occlusion. Furthermore, it requires simple computation and has real-time performance. However, it can not adjust the size of the tracking window adaptively, and has poor performance in tracking fast moving targets and those with maneuverable movement. The tracking algorithm based on mean shift and Kalman filter is proposed to improve the performance of the current mean shift tracker. According to the different interference circumstance, the Kalman filter prediction result and the mean shift tracking result are linear weighted by adopting different scale factors. Meanwhile, the target feature model is updated online to improve the tracking reliability. The experiments show that the improved algorithm is real-time and robust.In the active tracking of single camera part, whole structure of the system as well as every component module is separately introduced. The pan-tilt control module and the target relocation module are emphatically introduced. In the pan-tilt control module, the motion control model of the camera is established according to the camera's motion control parameters and the relative position of the target in the camera. The pan-tilt fuzzy control strategy is applied to keep the target in the center area of the scene. In order to realize the continuous tracking of the same target, the template matching algorithm by normalized cross-correlation is applied after the movement of the pan-tilt. When the location error occurs, the moving target detection algorithm based on the motion history image is applied to find the corresponding target again in the center area of the scene. The experiments show that the algorithm can track both the rigid targets and non-rigid ones well.In the multi-camera relay tracking part, some key problems and techniques of the multi-camera relay tracking are studied. The target handoff algorithm based on the FOV lines is emphatically introduced. The camera FOV lines generation algorithm based on the synchronous video is applied and the algorithm does not need the camera calibration and environment information, etc. When the value of the target visibility discrimination function changes from negative to positive, it means the target will soon present in the scene of the adjacent camera, then detects all the moving objects in the relative area and the tracking target is the one which is nearest to the FOV lines. The experiments show that the algorithm based on the FOV lines is simple, accurate and real-time.