Research On Moving Objects Detection And Tracking Methods In Intelligent Visual Surveillance System

Intelligent visual surveillance system can automatically analyze video sequences by the methods of computer vision and digital image processing without human intervention. The system can real-time detect, track and recognize moving objects in a scene. Furthermore, it can analyze and judge moving objects' behavior, give a description of the behaviors and actions, automatically discover some suspicious behaviors, identify irregular actions in the scene and alarm automatically in order to guide our actions and decisions.At present, there are many problems in the research and application of intelligent visual surveillance. Both domestic and overseas scholars devote themselves to the research and get a large number of achievements. On the basis of these achievements, this thesis researches the technologies of moving objects detection and tracking, which are critical steps in the intelligent visual surveillance. The main work is summarized as follows:(1) The LBP (Local Binary Pattern) operator is improved. LBP is a powerful mean of texture description. After counting the times of0jumping to1and1to0in a LBP's binary codes, the models, which have lower probability of occurrence, are combined. Therefore, the species of LBP texture are reduced, and the speed of feature matching is increased in moving objects detection and tracking algorithm based on the LBP texture.(2) A moving objects detection method based on a combination of improved local binary pattern texture and hue is proposed. Because both LBP texture and hue are not sensitive to shadows, they are used to describe a background. Then, the background is applied to the Gaussian mixture model, and the method can better resist shadows and the changes of background illumination.(3) A moving object detection algorithm based on a combination of optical flow and the three-frame difference is proposed. Because of the complexity of computing optical flow, the calculation of optical flow is simplified. A few pixels with certain characteristics are selected to compute optical flow information, which reduce the algorithm's complexity. However, because of selecting parts of representative pixels only, target area detected by the algorithm is not complete, so three-frame difference method is introduced as a supplement in order to get a relative complete target area without increasing too much computing complexity.(4) A Camshift tracking algorithm based on LBP texture and hue is proposed. Traditional Camshift algorithm is based on the color histogram of moving objects. When an object and its corresponding background have similar color, an interferential object and the tracked object have similar color or moving objects have shadows, the tracking accuracy can be greatly affected. The LBP texture and hue are combined to describe moving objects in order to solve these troubles because two features are not sensitive to the shadow. In addition, in the region with similar color between moving objects and background, LBP texture can often achieve certain effects; and in the region of lacking texture, the LBP texture performance is not good, but color can often achieve better results.(5) A moving object tracking algorithm based on Kalman filter and blob matching is proposed. Blob matching method matches the candidate target using the moving objects'shape characteristics. When there are a large number of moving objects, the method has slower speed because each blob must match with other objects. Meanwhile, the method does not apply to non-rigid object tracking, and it is difficult to continue to track objects if the objects'occlusion is existed. Kalman filter can predict the target's location in the next frame using the current frame's motion information. Before blob matching, Kalman filter is used, so the matching area can be limited in the predicted area by Kalman filter.This thesis is supported by the application foundation project of Yunnan province "Research on moving objects detection and tracking methods in intelligent visual surveillance system"(No.2011FB019).