Gait Feature Extraction Algorithm Based On Particle Filter Tracking

The so-called gait, which is the walking posture of people, is a biological behavioral feature which can be perceived only in the case of long-distance. It can achieve the purpose of recognising the identification of persons by the way people walk. It has become the focus of the study area at home and abroad recently.Gait recognition is a new biometrics recognition technology based on gait characteristics, including height, body shape and so on.It is mainly aimed at analysis and processing of moving image sequence including person. Generally it includes four stages:target segmentation, feature extraction, feature processing and recognition classification. It has a broad application prospects in virtual reality, visual surveillance, perceptual interface. Gait feature extraction is an important step in gait recognition. Gait characteristics already contains a lot at present, for example:Fourier descriptor with the scale, translation and rotation invariance which can represent the profile characteristics of the human body; limb angle containing a great deal of motion information which can express the identity of different people accurately; Reflective symmetry factor that can represent gait symmetry; contour shape context and so on. This paper verifises Fourier descriptor of the human body.The research of Intelligent Visual Surveillance has also been of widespread concern in recent years namely gives the monitoring system the ability to observe and analyze the content of the scene in order to make it more intelligent. It can analyze automatically the video sequence the camera records almost without human intervention, and timely respond. For example, it plays a very good role in the case of anomaly detection user-defined, or some unusual event.Moving target tracking process is a process by selecting the only characteristics of the target and searching the target location which matches the characteristics in the follow-up frame based on the objectives and their environment. It is not only the main data source of target motion and scene analysis, but also of help for target detection and recognition. At present there are many algorithms on the target tracking (object tracking).Fundamental difference among the various algorithms is the choice of the characteristics of the target description and the type of search matching algorithm. In this paper, we introduce the particle filter algorithm.The use of a combination of two techniques to achieve the improvement of the gait recognition rate is the main content and innovation of this article, the proposed method is:First, we use background subtraction method to extract the target from the background, and its Fourier descriptor features and then to use a particle filter algorithm to get a more accurate target location information in the process of movement, and on this basis, calculate Fourier descriptors to achieve superior gait recognition effect. It proves that the information extracted features with track is more accurate by the contrast of Fourier descriptor before and after filtering and the real target. This algorithm can improve the efficiency of target recognition of the human body.