Research On Coverage Control And Cooperative Processing Method For Video Sensor Networks

Coverage control and copperative processing are two kernels and hot topics in the research field of the target detecting application based on video sensor networks. The two issues in conventional sensor networks have been studied and analyzed intensively. Recently, with the appearance of novel video sensor networks, their distinct characteristics with directional sensing ability and a large amount of image/video data, make that many methods for conventional sensor networks are not suitable for video sensor networks. Thus, video sensor networks demand a series of innovative solutions, especially for coverage control and copperative processing. The thesis studies some fundamental issues in video sensor networks, such as sensing model, coverage control and copperative processing, especially focuses on area coverage enhancement, path coverage enhancement, layered copperation model and cooperative image processing based on visual correlation. Aiming at the problems mentioned above, we propose a series of new models and algorithms, carry out performance evaluation and simulation analysis. The main contributions of this thesis are as follows:(1) Based on the existing directional sensing model, we design a novel rotatable directional sensing model to describe the sensing ability of video sensors, moreover, we propose a layered cooperation model to abstract cooperative information processing for video sensor networks.(2) We study the issue of area coverage enhancement in video sensor networks with the assumption that video sensors have fixed locations and adjustable sensing directions. We present a centralized area coverage-enhancing algorithm by using graph theroy and computational geometry. We divide and conquer a video sensor network into several sub-areas to decrease the time conplexity of algorithm and quicken the area coverage-enhancing process. In addition, once the area covrage-enhancing scheme is calculated by the sink node, all of video sensors in the network will rotate their sensing directions at one time. Simulation results show that this centralized alogorithm can improve the area coverage performance of video sensor networks with small cost.(3) We present a distributed potential field based coverage enhancement algorithm for video sensor networks. By introduing the concept of "centroid", we translate the pending problem into centroid uniform distribution problem by moving centroid points around the video node instead of rotating sensing directions. Multiple centroid points repel each other to eliminate the sensing overlapping regions and coverage holes. Simulation results show that this distributed algorithm can significantly improve area coverage performance and enhance the target detecting ability.(4) For object tracking application using video sensor networks, we propose a potential field based path coverage-enhancing algorithm. By introducing the concepts of "centroid" and "trackpoint", we construct virtual potential field to study on the force laws to govern the interaction between centroid-centroid and centroid-trackpoint. This algorithm maximizes path coverage along with target trajectory while minimizing the overlapping sensing area among multiple neighboring video sensors thus achieve adequate and efficient path coverage. Extensive simulation results show that this algorithm can effectively enhance path coverage performance with small cost, thus improve the object tracking qaulity provided by video sensor networks.(5) Based on visual correlations, we study a cooperative information processing method for video sensor networks. Given the severe resource constraints on individual video sensors and the redundant visual information among video sensors, we adopt a divide-and-conquer method to partition a sensing task among highly correlated ones. In particular, we propose a cooperative image proceesing method according to the epipolar line constraint. Sink node fuses the received multiple partial images, thus reconstruct a complete visual scene. Experimental results show that our method is more efficient than the non-cooperative ones in reducing transmission workload, saving network energy and performing visual monitoring task.