Research On Target Localization And Tracking In Video Sensor Networks

The target localization and tracking is a fundamental technique for many applications in wireless sensor networks, thus it is worthy to be studied. In traditional wireless sensor networks, target localization and tracking are well investigated, and there are many existing works. On the other hand, a new type of wireless sensor network, video sensor network, becomes a research focus recently. Because of the rich sensing information, video sensor networks have more advantages to implement the target localization and tracking than the traditional wireless sensor networks.In traditional wireless sensor networks, the researches on target localization and tracking are always based on the infrared ray, sound, or quiver. On the other hand, in the area of traditional video surveillance, the researches on target localization and tracking only focus on the video content processing without considering the aspect of networking. These traditional methods about target localization and tracking cannot be applied for video sensor networks directly. Therefore, it is necessary to study the target localization and tracking problem on the basis of the video information and the features of sensor networks.This thesis aims at the typical scene of target localization and tracking in video sensor networks, and proposes a serial of novel models and methods from three different aspects: networks deployment and coverage, network topology control, and the localization and tracking algorithms. Our proposed models and methods are based on the mathematic analysis, and verified by extensive simulations and experiments. The main contributions of this thesis are as follows:(1) Localization-oriented sensing model for video sensor nodes. We reasonably abstract the sensing area of a video sensor node. Furthermore, by using the perspective projection model and the Gaussian noise model, we build the geometrical relationship between the measurement and the target location. Then, we design a localization-oriented sensing model for video sensor nodes which is the basic model of the target localization and tracking problem.(2) Localization-oriented coverage. The traditional methods about the coverage always aim at target detection, and then they are inapplicable for target localization. Based on the localization-oriented sensing model, we propose a novel concept, localization-oriented coverage. Moreover, by using the deployment strategy which follows the 2-dimensional Poisson point process, we derive the mathematic relationship between K coverage probability and the nodes density. Then, we utilize the Bayesian estimation and Monte Carlo simulations to build the relationship model among the nodes density, the localization-oriented coverage probability, and the FoV (field of view) of video sensors. According to this relationship model, we can estimate the size of deployed video sensor network for a given localization-oriented coverage probability.(3) Nodes selection for localization and tracking. For the localization and tracking applications of video sensor networks, putting all the video sensors in the working mode is not necessary and increases the cost of the network. Then, we study how to use a nodes selection scheme to balance the localization quality and the cost of network energy. We divide the target localization process into two phases: detecting phase and locating phase, and design two nodes selection schemes for the two phase, respectively. For the detecting phase, we utilize the detecting-oriented coverage probability and the localization-oriented coverage probability to derive the relationship between the density of the deployed nodes and the density of the working nodes. Based on the relationship about densities, we design a nodes density control method according to PEAS (Probing Environment and Adaptive Sleeping) mechanism. Our nodes density control method can be implied for selecting the set of working node to maintain required coverage probability. For the locating phase, we utilize the information entropy of the probability density function for the target location to set up the utility function. Meanwhile, we use the energy cost model of video sensor nodes to define the cost function. According to the utility and cost functions, we map the nodes selection problem to an optimization problem, and then develop an optimal selection algorithm to minimize the energy cost while satisfying the accuracy requirement of localization.(4) The multi-node collaborative tracking algorithm and the corresponding implementation mechanism. We build up the target state equation according to the target motion model, and build up the measure equation according to the localization-oriented sensing model. Based on these equations, we apply sequential Monte Carlo method to estimate the target location during the tracking process. In order to implement the tracking algorithm efficiently, we further propose a dynamic cluster based mechanism. Our mechanism include: a) election of cluster head, and b) selection of cluster members. We derive the probability density function of target location, and then elect the next cluster head according to the probability density function and the neighboring nodes' locations. In order to select the cluster members, we build up a selection standard by combining the target detecting probability and the posterior probability density function. The selected video sensors send their measurements to the cluster head, and update the posterior probability density function of target state for estimating the target location.