Research On Human Face Detection And Tracking Algorithms Based On Video Sequences

Human face detection and tracking is a key face information processing technology. The human face image processing field includes the researches on human face identification, pose estimation, expression identification, video monitoring, etc. All these researches involve human face detection and tracking. Based on the domestic and foreign academic papers and research reports on the human face detection and tracking, this paper describes the in-depth study of the human face detection and tracking algorithms. In addition, it describes the design and implementation of the human face detection and tracking method based on the Camshift and particle filter algorithms with the assistance of the research results of human face detection and tracking technology. This paper describes the following contents:1. To make up for the disadvantages of the traditional Camshift algorithm, the Camshift algorithm based on the Kalman filter and the Camshift algorithm based on the AdaBoost detection are put forwarded. The Kalman-Camshift algorithm is more robust in the actual situations where the background color is close to the target color and where the target is occluded. This algorithm improves the tracking matching degree and accuracy. The AdaBoost-Camshift algorithm uses the human face detection to initialize the tracking window and implements full-automatic and real-time tracking for multiple targets.2. By comparing the status estimation of the particle filter algorithm and that of the extended Kalman algorithm, this paper analyzes the advantages of the particle filter algorithm in the non-Gaussian and non-stationary model. This paper also describes the advantages of the particle filter algorithm and the Mean Shift algorithm and put forwards the combination of these two algorithms. With the real-timing of the Mean Shift algorithm and the robustness of the particle filter algorithm, such combination can solve the problem of incorrect tracking or even target loss when the target human face is occluded to a great extent. The experiment result proves the real-timing and robustness of the combined algorithm.The Microsoft Visual C++ and the Open Source Computer Vision Library (OpenCV) are used to verify the feasibility of the human face detection and tracking algorithms that are put forward in this paper in different scenarios.