Human behavior analysis in a video scene is based on motion feature extraction and recognition, many information is hidden behind gesture, sudden motion and walking, many research works tried to model and then recognize human behavior through motion analysis. In our work we will focus on motion extraction and tracking with some enhancements comparing to previous approaches. The first phase of behavior analysis is the extraction of regions in the video in motion. The second phase is devoted to motion tracking using Kalman filter. Performances evaluation are based on both visual quality and numerical values of MSE(Mean Squared Error).
Tracking moving objects is an area increasingly known in computer vision field. It plays a very important role in human-computer interaction. In this context we have developed a hand tracking and gesture recognition system that allows interaction with the machine in an intuitive and natural way. To ensure the tracking we apply the Kalman filter and detect the optimal points of the hand in order to determine the gesture expressed by user.