Study On The Method Of Face Detection And Tracking Number

This thesis studies video-based face detection and tracking. Some improvements are putted forward regarding the disadvantage of the Camshift algorithm and AdaBoost algorithm.Firstly, AdaBoost face detection algorithm is discussed according to Hidden Markov model. To resolve the problem of low accuracy to AdaBoost algorithm face detection, the improved AdaBoost algorithm combing rotary face location is proposed. This paper is focused on the rotating face location method and the method of image edge detection which are combined with the algorithm, such as the Robert operator, Prewitt operator, LOG operator, Krisch operator,Laplacian operator and so on.Then, a research has been made on Camshift face tracking algorithm referring to the color histogram as its main feature. In the progress of tracking, Camshift algorithm can effectively avoid the interference of light dark color information and shows great advantage on the point of anti-jamming. However, the target is lost when the target moves swiftly and is invisible.To overcome the defect, the paper proposes supreme Camshift algorithm. Kalman combining with Kalman filter. Kalman filter is a linear recursive operation process, which uses the system’s last state to make an optimal estimation on the next state. making the estimation of optimal results In the prediction and update constantly.Eventually, according to the concept of Kalman filter, the passage proposes the Camshift window size prediction according to the forecast results to modify Camshift tracking window size.In tracking the next frame only needs to search the rectangular box area predicted after the identification.The algorithm greatly reduce the iteration time spent on initializing the search window and searching the unnecessary area and ensure the real-time tracking.