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Rescarch Of Target Continuous Tracking In Multi-Camera Surveillance Network

Posted on:2016-10-03Degree:DoctorType:Dissertation
Country:ChinaCandidate:W H DongFull Text:PDF
GTID:1108330461985413Subject:Pattern Recognition and Intelligent Systems
Abstract/Summary:Request the full-text of this thesis
With the development of information technology and the increasing demands of social security, the traditional surveillance technologies can not meet people’s needs. Intelligent and automated surveillance technologies are becoming the main development direction. As an important part of intelligent surveillance, target continuous tracking in multi-camera surveillance network becomes the hot research spot, which has an important theoretical significance and extensive application value. Target continuous tracking in multi-camera surveillance netwok not only includes visual tracking with single camera, but also includes the target handoff among cameras. Compared with the traditional visual tracking with single camera, many new difficulties are brought due to the increasing number of cameras. Such as the complexity of the surveilled scenes, the different parameters of cameras, the different traces of the targets, and so on. How to overcome these difficulties and achieve accurate target continuous tracking in the multi-camera network is one of the urgent tasks in intelligent surveillance field.This dissertation focuses on target continuous tracking method in multi-camera surveillance network with non-overlapping view fields. The main content includes the robust visual tracking algorithm in single camera, the topology learning method of camera views, the appearance model construction of the tracked target and target matching in multi-camera network, and the accurate target handoff among cameras.The main research work and the results of this dissertation are as follows:1) Visual tracking method in single camera is studied and multi-feature sparse representation based visual tracking method is proposed. The algorithm uses particle filter as the framework, including observation model and state model. The observation model is constructed by multi-feature sparse representation. The state model uses Gaussian distribution with six parameters. In order to adapt the variations of the target during tracking, the adaptive template update algorithm is added to the tracking algorithm. Experimental results show that the visual tracking algorithm can handle many complexity conditions, such as illumination variation, occlusion, pose variation and cluttered background, and achieve accurate and effective tracking results.2) Topology learning of the multi-camera views with non-overlapping view is studied. The topology of the multi-camera views includes entrance/exit zones (nodes), transition time distribution between the nodes and the links between the nodes. The topology learning method of the multi-camera views is proposed through the statistical information of target traces in the surveillance videos. Firstly, the entrance/exit zones are learned by Gaussian clustering. Then, the transition time distribution between two nodes is gotten by accumulated cross correlation function and Gaussian fitting. Finally, the false links between the nodes are removed by mutual information theory and the accurate topology is recovered. According to the topology of the multi-camera views, the time-space information can be gotten,which can be used as a reference for camera scheduling in the multi-camera target continuous tracking (For example, when the target arrives at the exit node of one camera, the handoff camera and the travel time range can be gotten from the topology).3) The appearance variation characteristics of the target in the multi-camera network are also studied. The feature tree based appearance model construction method and target matching method are proposed in this dissertation. Based on the research of appearance model construction methods and key problems existing, combining the characteristics of the target continuous tracking in multi-camera surveillance network, the dissertation presents the appearance model construction method and target matching method based on the feature trees. The trees are constructed by unsupervised clustering with different features, which can describe the target comprehensively. When the appearance varies during the tracking, the feature trees are updated by the new samples of the target. The feature trees not only can adapt to the appearance variation but also can recall the former appearance of the target. Feature tree voting is used for target matching. The similarity between each candidate and the target should be calculated in every layer of the feature tree. The screening method can greatly reduce the computational complexity and improve the effiency of the matching.4) After studying the key problems of the target handoff among cameras, the target handoff algorithm in multi-camera based on inheriting and learning is proposed in this dissertation. The accurate target handoff among cameras is the key factor for the successful continuous tracking in the multi-camera network. By fusing the time-space information and the appearance model, the target handoff algorithm based on inheriting and learning is proposed. This algorithm integrates the visual tracking with single camera, the topology of the camera view fields and the target appearance model into the whole target handoff framework. Firstly, the space-time information is obtained from the topology to eliminate the disturbances of the uncertain factors. When the target is determined, it is tracked using the single camera visual tracking method and the appearance model in this camera is constructed. Once the target handoff is triggered, the appearance of the target will be transferred to the handoff camera. Then, the handoff camera searches the target at the corresponding entrance zone during the transition time interval using the appearance model. If the target is detected, the handoff camera will continue to track the target in its field of view and the target appearance model will be updated using the tracking results. With the continuous target tracking in the multi-camera network, the appearance model of the target will be more and more accurate, which will make the target handoff and tracking smoother and smoother.In conclusion, the research works of this dissertation are useful attemptes in many aspects of target continuous tracking in multi-camera surveillance network and good results are obtained. These works will provide a new idea for the target continuous tracking in multi-camera surveillance network.
Keywords/Search Tags:Visual tracking, Multi-camera network, Topology, Target handoff, Appearance model, Inheriting and learning
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