Analysis Methods Of Video Events Based On Images

The technology of video processing has promising future in science research and engineering application. Due to the continuous working of the equipments, a lot of video sequences are produced and need dealing with. How to analyze the video events rapidly and exactly is a hotspot which captures much attention. The thesis focuses on the analysis methods of the video events. The research includes the following aspects: division of the video sequence, moving targets detection and the tracking, and semantic events detection. Generally there are two kinds of reasons which cause the events: the motion or the change of the targets, and the change of the whole scene. So the corresponding research also outspread in two sides, namely events detection based on the restrictions of the targets'characters and events detection based on the restrictions of complex conditions in soccer games.In division of the video sequences, a novel inter-frame-information-based approach is proposed to detect the sections whose spatio-temporal distribution is obviously different from the average distribution of the whole video sequence. In this approach, the color information, motion information and moving ratio information are used to describe both the change of the scene and the changes of the targets in the scene, and divide the long video sequence into sections. This approach does not need to extract the key frames, and overcomes the ineffective flaw of the shot boundary detection methods when the background is stable. The processing efficiency is also improved at the same time.In the moving targets detection and tracking, a novel framework is proposed to process the video when the background is either stable or dynamic. It consists of three aspects: background type recognition based on difference information of the adjacent frames, targets tracking with the color-based particle filter, and objects precise detection by using the strategies which is suitable for special background types. In stable background, a top-to-bottom local hierarchical GMM (LHGMM) is proposed to detect the targets accurately. This approach only detects the targets in local regions and can improve both the veracity and the efficiency. On the other hand, when the background is dynamic, an adaptive level set (ALS) method is proposed to get the precise external contour of the targets. This method can set the zero level set automatically and evolution the level set curve in given areas. So this method can get the accurate external contour easily.In the events detection based on the restrictions of the targets'characters, the characters selection approach based on fuzzy-rough techniques is firstly proposed. This approach can be used to select the characters combined with the application conditions, and describe the events by using the spatio-temporal information. Then, the events analysis is carried out in three special aspects: (1) A region-based abnormal behaviors detection approach of the road-across pedestrian is proposed to detect the abnormality by using both the region-based segmentation information and the change information of the targets. This approach can satisfy the on-line video process requirement. (2) A contour-based approach is proposed to analyze the human poses. This approach uses the periodic characters of the moving human to categorize the human poses. (3) A motion-based detection approach of the video events in the traffic crossing scene is proposed to detect the abnormal behaviors by using the moving trajectories. With the self-acting study on moving trajectories, the approach can get the regions and the moving direction of the moving trajectories, and detect the abnormal behaviors. So the surveillance systems can work on-line.In the events detection based on the restrictions of the complex conditions in soccer games, a hierarchical classification tree is proposed to classify the video clips rapidly and effectively only by using simple low-level characters. Then based on the clips classification, a temporal structures-based approach is proposed. This approach can detect the prior-defined exciting events by using both fixed temporal structure of clips and the low-level characters, such as motion vector and so on. Neither the tracking of the targets nor the prior training is necessary because this approach uses the restrictions of the scene directly, and this can improve both the processing quality and the efficiency.