Study On Human Motion Analysis And Its Applications In Video

Vision-based human motion analysis aims at detecting, identifying and tracking moving people from video sequences, and furthermore understanding and describing human activities. It is one of the most active research topics in computer vision. Human motion analysis sytem usually involves image preprocessing, moving object detection and identification, object tracking, and human behavior understanding and description. Although many research results have been achieved on object detection and tracking, there are still a lot of issues to be settled in behavior understanding and description.In this dissertation, image smoothing, adaptability to scale changes of tracking algorithm and human behavior understanding are considered. The main research achievements include:An anisotropic diffusion coefficient equation based on visual masking effect is proposed, which improves the performance of anisotropic diffusion image smoothing method. The noise in image sequences is removed effectively by the improved smoothing method. It is compared with several existed image smoothing algorithms. Experimental results show that not only the smoothed image by our method is better according to the signal-to-noise ratio and the mean structure similarity, but the convergence speed of our method is faster.The information measurement of multi-scale images in scale space is investigated, based on which an object tracking algorithm with self-updating tracking window is proposed. The algorithm improves the Mean-Shift and partical filtering tracking methods and makes the moving objects with increasing scales or decreasing scales are both tracked exactly. And the improved tracking algorithms also run in real time.An unsupervised temporal segmentation algorithm of human motion sequences is proposed, and then the obtained activity segments are recognized by HMM. However, most of the existed activity recognition algorithms are conducted on the segmented activities. In this dissertation, moving human blobs are described by a new feature which is a compact contour point set. Then temporal segmentation of human motion sequences is achieved by detecting the change point of the intrinsic dimensionality of feature vector, which is estimated by PCA. Experiments on two commonly used public databases demonstrate the effectiveness of our algorithm.Videos consist of multiple types of human actions are collected by a Panasonic camera (WV-CW960) mounted on the wall, and the experimental environment coincides with the real surveillance system. An activity data set called OwnSet is constructed by the collected videos, on which the subsequent activity recognition and abnormality detection algorithms are conducted. There are six kinds of actions in OwnSet, which are walk, jogging, sitting down, squatting down, falling down, and immovability (including stand, squat, and sitting).A system to recognize multiple kinds of activities is presented. It consists of four modules:video collection, moving objects detection and location, feature extraction, and activity classification. Background subtraction is employed to detecting moving persons. Two new features named Motion Energy Sequences (MES) and Contour Code of Motion Energy Image (CCMEI) are extracted. In the activity recognition module, the hierarchical classification thought is introduced. Two support vector machine decision-tree classifiers based on apriori knowledge and clustering results respectively are used to recognize multiple kinds of actions, and the latter classifier is more universal than the former. Experimental results on the OwnSet and a public database show that the recognition accuracy of our algorithm is bigger than other algorithms and it is roubst to incomplete human blob, heavy shadows, as well as human appearance variation.The application of human motion analysis in intelligent surveillance is studied in this paper. Abnormality detection from multiple daily activities is conducted. Alarms will be issued in time when the olders, the children or the patients encounter a fall. An abnormality detection algorithm based on a combined classifier is presented to detect fall from the six types of actions in OwnSet. Since the causes and dangers of on-marching falling down and in-place falling down are usually different, another detection algorithm based on support vector machine is proposed. Experimental results demonstrate that both algorithms have good recognition performance and real-time performance. Therefore, the proposed algorithms are easily extended in real applications.Finally, some difficult problems to be settled in human motion analysis system as well as the future work are listed out.