Object Tracking Based On Feature Matching And Object Behavior Recognition Based On Support Vector Machine

Behavior recognition of human movement is one of the central issue of theintelligent video surveillance. It enables the computer automatically analyzes thevideo content and lets the machine have the same subjective judgment with human.The main job is to detect and extract the moving object from the video sequence, andto track the object. And then the computer can analysis or descript the category of themoving object.The main work of this paper is to study the behavior recognition based on videoprocessing technology. There are three aspects in this paper: moving object detectionand extraction、moving object tracking and moving objects behavior recognition.On the aspect of moving object detection and extraction, propose a method that is theadaptive selection in background window of a threshold when detect changes inmotion information based on higher order statistics method. This paper will apply theCumulative inter-frame difference method based on Secondary inter-frame differencemethod to extract object. On the aspect of moving object tracking, a method based onthe combination of feature matching and Kalman filter is proposed. On the aspect ofmoving object behavior recognition, put forward a method based on Support VectorMachine to recognize the behavior of moving object and propose my owncharacterization methods.The process of moving object detection and extraction: First of all, calculate thecumulative inter-frame difference before and after the current frame. Secondly, detectthe changes of movement respectively and filter out the noises. At last, take theintersection of the cumulative inter-frame difference before and after the currentframe to extract the moving object. When apply the method of detecting changes inmotion information based on higher order statistics, this paper proposes a method thatis the adaptive selection in background window of a threshold, it can enable thesystem select background window of a threshold adaptively.The process of moving object tracking: After the detection and extraction, we getthe moving objects. Extract the position, shape and brightness characteristics of theseobjects. Use these various characteristics compose the feature vector of each objectand label each object to start the tracking. Detect and extract the moving objects incurrent frame of the video, match the feature of the moving objects between currentframe and the frame in front of it. Since there maybe objects failed to match, wediscuss the possible condition, such as occlusion、disappearance and the appearance ofa new object. First we discuss the condition of occlusion. If the occurrence ofocclusion is judged between objects, use the Kalman filter algorithm to predict thepositions of these objects in the current frame, and match their features. And then ifthere are objects of the front frame failed to match in current frame, also use theKalman filter algorithm to predict the positions of these objects of the front frame inthe current frame and match their features. However, if there are still objects failed tomatch in current frame, it may be the appearance of a new object and label this kind of objects. After the end of the tracking in each frame, update the feature informationof the moving objects to carry out a subsequent frame and track the objects.The process of moving object behavior recognition: This paper proposes the SupportVector Machine as the class classification system according to characteristics of thetracking objects. In the training of the classifier, the paper presents a time-basedcharacteristic to describe the behavior of objects. Select the position feature of themoving objects in each frame and calculate the moving distance of the objectsbetween two consecutive frames. Study the statistical properties of the distance as thefeature. Through the establishment of database with the feature of the samples,analyze the classification mechanism and design the classification system to achieve agood classification effect. At last through the simulation results, verify theclassification accuracy of this article behavior recognition system.