Research On Moving Object Detection And Tracking Methods For Intelligent Video Surveillance

With the widely application of static camera video surveillance system, more and more surveillance videos are produced. One has to manage and real-time mine information and knowledge of interest from the large-scale videos, in order to realize intelligent video surveillance. The moving object detection and tracking methods are the most basic and important technology in the area of intelligent video surveillance, and are the key to realizing real-time intelligent video surveillance. The traditional methods, which are not fit for complex environment and can't meet accuracy and real time at the same time, are common designed for special scenes.Due to the requirements above, this thesis focuses on the rigorous and real-time methods for both detecting and tracking moving objects within video sequences acquired by monocular and static camera. The primary work including:(1) The scene of video surveillance system acquired by static camera is partitioned into two parts: the simple scene and the complex scene, which is based on the complex degree of video background model. A moving object detection method based on area partition for simple scene is proposed and improves the detection accuracy and low illumination sensitivity, also a fast convergent Gaussian Mixture Model for complex scene is proposed and improves the convergent speed and time efficiency compared with the traditional Gaussian Mixture Model.(2) Moving object detection can acquire the binary image of background and objects. A fast path-based binary image clustering method and a fast holes filling method are proposed to carry out objects segmentation and noise elimination. Compared with traditional methods, the proposed methods account for the low and unstable time efficiency and enhance the clustering and filling effect.(3) Most moving objects in surveillance video are human or vehicles. Human's 3D upright ellipsoid model does well in solving human's part sheltered and shadow problem, but that vehicle model is used to solve vehicle's part sheltered and shadow problem will be a time-consuming work because of the complexity of the vehicle model. To this end, in this thesis, a part sheltered vehicle segmentation and shadow elimination method based on Morphology which improves the time efficiency is proposed, and the method is real-time.(4) A moving object tracking method based on adaptive particle filter, which is not dependent on fixed moving model and improves the accuracy, is proposed. A moving object tracking method based on forecasting and character matching, which picks up moving object's figure, geometry and color characters, and improves the accuracy of particle filter, is proposed. The experimental results corresponding to each method are presented and the efficiencies of the methods are evaluated and discussed under the criterion of accuracy and real time. The researches of this thesis will make contribution to the technology of moving object detection and tracking in surveillance video acquired by static camera.