Research On Moving Objects Detection And Tracking Technology In Video

Moving objects detection and tracking base on static video is one of the fundamental issues in the computer vision and other relative fields, algorithm which has excellent performance can give powerful support to high level applications such as intelligent video surveillance, pattern recognition, and intelligent video conference and so on. But due to the complexity and mutability of real environment, and also because of the limitation of image sensor's resolution, resulting in that lots of difficulties exist in detecting and tracking moving objects in complex background situation.For the above reasons, moving objects detection and tracking technologies in video are researched in this paper, and several new algorithms are put forward on the basis of summarizing forefathers. The state of the art at home and abroad is presented in the first chapter. Two novel moving objects detection algorithms are proposed in the second and third chapter of this paper respectively, the first is moving objects detection by region Gaussian model base on self-organization mapping, the second is research on adaptive graph-cut algorithm to video moving objects segmentation. The Rao-Blackwellized Monte Carlo data association for multiple targets tracking algorithm is introduced in the forth chapter. A summary and outlook of this paper is given in the fifth chapter. The algorithms described in the second to forth chapter are introduced as below.The moving objects detection by region Gaussian model base on self-organization mapping algorithm, combined single Gaussian model with region Gaussian model to make up a cascading detection method, and the"competition & cooperation"mechanism was introduced in the step of region Gaussian model updating. This method not only could detect moving objects efficiently at the scene of shaking leaves, waving water surface and other complex background, but also could guarantee the integrity of detected objects. Through the experiments of PETS2002 and Water Surface image sequences, the characteristic of proposed algorithm was validated in various complex background environments.The adaptive graph-cut algorithm to video moving objects segmentation, through the Kalman prediction of the number of moving objects pixels and foreground-background adjoining pixels, and adaptive update of the nodes flow, the graph-cut algorithm is successfully applied to video moving objects segmentation. It achieves to continuous global optimization segmentation of video moving object. Experimental results show that the quantitative detection indicators of this algorithm perform very well in complex background conditions.The Rao-Blackwellized Monte Carlo Data Association for Multiple Target Tracking algorithm couples with Kalman prediction, which reduces the particles and increases tracking precision outstandingly compares with general particle filter. This algorithm also can cope with disappearance, shelter, and appearance of unknown number targets. The tracking results of simulating scenes are given at the end.Comparing with other algorithms such as single Gaussian and multiple Gaussians, these two moving objects detection algorithms proposed in this paper get good performance in detection precision and ability in dealing with background noises. The multiple objects tracking algorithm described in the forth chapter can track unknown numbers moving objects with less amount particles, which improves tracking speed and precision outstandingly.