Research On The Key Techniques Of Embedded Smart Camera Network

As video surveillance system is proliferating worldwide, the application through intelligent video surveillance (IVS) system to achieve interrupting or preventing acts of crime or terrorism is becoming more and more important. Because of the limitation of processing and communication, the center-based IVS cannot adapt to the large-scale applications. Implementation of the distributed intelligent video surveillance (DIVS) is a solution to larger-scale video surveillance application. The embedded smart cameras network with the ability to provide an automatic interpretation of scenes is the primary component of DIVS. The research of embedded smart camera network is critical in implementing DIVS system. The thesis is organized as follows:1. The design of high performance embedded smart cameraA novel high performance embedded smart camera is proposed in this thesis. The design of the embedded smart camera is accomplished based on a dual-core DSP processor and a software framework for multi IVS task is also discussed.2. The visual analysis algorithms based on embedded smart cameraUnder a particular computing environment, the implementation of several visual analysis algorithms with the embedded smart camera is discussed. It includes moving object detection, shadow removal and moving object classification. A novel moving object tracking algorithm based on local image descriptor is presented. The tracking task is accomplished by locating the target in the search space of the local image descriptor which is created by the key points of image. The method is stable even there are scale and appearance changes to the tracked target.3. The organization architecture of the embedded smart networkIn the thesis the issues about camera group are investigated thoroughly. A fast view matching based method is proposed to detect the overlapped areas between cameras. A camera grouping approach based on Markov random filled is also proposed, the grouping is accomplished by the belief propagation algorithm. Combing these two algorithms, a camera-group based organization-architecture of embedded smart network including a state machine and camera communicating protocol is present in the thesis.