The Research On Detecting And Tracking Of Multiple Objects In Real-time Video

The aim of Intelligent Visual Surveillance is to make it possible using computer vision method, in the situation of not needing interactive operation, by automatically analyzing the image sequences captured by cameras, computer can localize, track and recognize moving objects in dynamic scenes, analyze and judge the behavior of the objects, thereby not only can complete daily management,but also can react to exception in time. One main research content is motion segmentation and track.This thesis mainly research on the method of detecting and tracking of multiple objects in stationary scenes. It analyzes the common method of detecting objects, and presents a algorithm of detecting objects which combine background subtraction with time difference to improve the performance of detecting. It researches the background model based on gradient and chromaticity which can update background in real-time, which provide exact information of objects to next module.In tracking objects module, the thesis applies Kalman filter to estimate and predict the objects'moving state. In association module, the Mahalanobis distance is utilized to get the similarity among objects."Matching matrix"is presented to get the best matching object. Then, the thesis analyzes how to use color as a cue to track. The color histogram and intersecting algorithm is applied. When objects are occluded, many patches are produced according to object's moving probability, the association module based on objects'model define which is the best patch can represents the occluded object. Then the occluded object's visible parts is segmented by probability. At last, the thesis presents different moving cases and gives corresponding flowchart.According to the experimental results in real-time video, the methods which the thesis presents are proved that it can realize detecting and tracking objects under their different moving cases in stationary scenes.