Research On Analysis And Recognition About Abnormal Behavior Of Moving Human In Video Sequences

Intelligent Video Surveillance (IVS) is a kind of technology to achieve automatic video analysis with computer techniques. As an effective means of defense and security, IVS systems are being more and more popular. The analysis and recognition of pedestrian's abnormal behavior in the video sequence, a research objective of IVS, has gradually drawn the attention in the field of IVS. The IVS system based on the analysis of abnormal behavior can not only ignore a large number of useless information, which guarantees the high efficiency in the security protection, but also save a lot of human and material resources, which brings great economic benefits to the whole society. In addition, it is also able to achieve real-time alarming to eliminate the lags in tradition monitoring systems. This thesis, in both the theoretical and the practical perspective, probes into abnormal gait recognition with the videos as input. The following proposed algorithm and methods are carried out in practice application. The main contributions of this thesis are summarized as follows:1) The classification method of the minimal standard deviation based on template is presented, which can solve the problem that different people have their special gait, and thus implement the classification actions better. Around the fact that people's actions are a posture set in time sequence, the image sequences are firstly converted to a set of static shape mode in use of the periodicity of the human movement. And then in the recognition process the mode is compared with behavior of pre-stored samples. In the establishment of standard library stage, the minimal similar degree template generation algorithms is presented, which can solve the template representation problem. Take the Procrustes medium value shape distance as the similarity standard of the image, we can find out the behavior which has the minimal standard deviation from the standard database in one single test cycle. Experiments prove that the minimal standard deviation algorithm can meet the non-continuous identification requirements, and overcome sensitivity of the time interval changes in template matching. The weighted average of the maximal mean value and the minimal standard deviation algorithm is proposed, which take the Zernike speed matrix distance as the similarity standard of the image, can solve the uncertain speed motion and increase the precision of movement classification fundamentally.2) A Fuzzy Associative Memory (FAM) networks using behavior classification is proposed, which solve the problem between their movements and appearance, so that the computer recognition process can get closer to the people's thought processes. Each static posture is treated as a state, these states are linked through some kind of probability. Any movement sequences can be treated as a traversal process of a static posture between the different states. The joint probability is calculated during the traversal, and the maximum value is chosen as the behavior classification standards. Pedestrian contour is used as a feature with study and identify in low-level. Behavior matrix for each category is generated with HMMs study. The motion classification is deduced by the knowledge of FAM network. In different circumstances, FAM network knowledge can be updated at any time through learning. This thesis uses four layers fuzzy neural network model, which is a system with multiple input and single output. It has nine input unit, which are eight standard deviations of each action and one centroid. Each input is a membership function. The first layer of the system is the input layer. The second layer is the membership function, whose effect is to turn the input into the membership degree. In the third layer, each node represents a rule that comes from the study algorithm. The relationship between present nodes and previous nodes relies on the rules; the node function relies on the application of the rules. The fourth layer is the output layer. In the system with multiple input and single output, the beginning weight is the membership degree of the rules. Then behavior breed has been obtained through the iteration algorithm. Experimental results show that the algorithm has given a good recognition results, and has a certain degree of noise immunity.3) Abnormal behavior of pedestrian detection based on Fuzzy theory is proposed, which solve the problem that the definition of abnormal behavior should be defined before determine, thus reach the direct judgments about abnormal behavior. Subject to certain scenes, scholars in and abroad have proposed methods based on statistical techniques, physical parameters, time-pace movement and model separately. The method of statistical techniques is robust and has fast calculation speed. The method using physical parameters is understandable and observation angle independent, while it depends on the parameter of the recovered scene. The time-space movement method can reveal the character of the time and space, but can easily be disturbed by the noise. The model method has the problem that we can hardly get the precise model from the video. It also calls for massive amount of processing. To detect and tract the particular moving targets in specific environment, our method is unrelated with the scenes. Firstly a simplified human joints model has been established to model the human body. Then a fuzzification function is designed with the variety of body's trunk and limbs contour angles. Thirdly an abnormal behavior discrimination algorithm based on fuzzy theory is proposed, which applies fuzzy membership of the pedestrian's trunk and limbs to get the overall degree of the anomaly. Finally in the reality of the system, a combined method of center of mass and fuzzy discriminant is presented. Fuzzy discriminant can detect irregularities and implements initiative analysis to body behavior in the visual surveillance. Therefore, abnormal behaviors can be recognized and alarmed. The results show that the new algorithm has a high recognition rate.4) A method of abnormal action recognition in variable scenarios is proposed, which eliminate the ambiguity that one single action of the same person can lead to different comprehension under different circumstances. In the monitoring application, different scenarios have different exceptions to determine the rules of the algorithm and can be applied to determine abnormalities in a variety of places. There are different understanding results in different scenarios even if the same person's action in visual analysis. In order to determine whether the behavior is abnormal in different scenarios, a double-layer bag-of-words model is proposed to solve the problem in our surveillance system. The video information is processed in the first layer of bag-of-words, and the information of scenario-action text words is included in the second one. A video sequence is represented as a collection of spatial-temporal codebook by extracting space-time interest points. The behavior characteristic is represented as a collection of behavior text words in special scenarios. Probabilistic Latent Semantic Analysis (pLSA) model is adopted to automatically learn the probability distributions of spatial-temporal words and the topics correspond to human action categories. PLSA can also learn the probability distributions of the motion text words in a scenario with supervisor and the topics correspond to anomalous or normal actions. The algorithm can categorize the human anomalous or normal action contained in the special occasion to a novel video sequence after being trained.5) A method of automatically selecting characteristics for pedestrian tracking is proposed. The paper presents an algorithm of cover ration which can select the largest feature region of human body automatically to improve the way that the tracking area is pre-designated in current pedestrian tracking methods. Since human motion belongs to the time-varying and space-varying problem of a non-rigid object, the outline of a pedestrian in motion is changing constantly. The algorithm of cover ration can find the best area of the pedestrian which can be tracked at any time automatically. Then it can select a weighted color histogram within the feature region as tracking features and take the similarity of color model described by Bhattacharyya distance as a strong evidence of particle weight. Finally, in the framework of particle filter theory, the real-time tracking of pedestrians can be achieved automatically.