Research On Boosting Based Real-time Face Detection And Tracking Algorithm

With the development of artificial intelligence and computer science, Machine Vision Technology has made considerable progress, and attracted more and more attention. At present, objects detection, identification and tracking according to machine vision technology, have been widely used in various economic aspects, such as national defense, aviation and navigation, medical treatment and health care, security monitoring, and so on. Face detection and tracking is one of the most important applications. The purpose of face detection is to detect if there are the faces occurred in the surveillant area. If that, then determine the information of location and size of the faces. Object tracking is also an important application branch of Computer Vision. The goal of tracking is to recognize target objects from the background and extract features, then to depict the moving locus of them. How to track the objects precisely and fast is the key problem of objects tracking system.In this paper, a method combined face detection with objects tracking effectively was introduced to realize real-time face moving depiction. The system consists of two parts: face detection and face tracking. By analyzing the algorithm of detecting and tracking, this paper brings out reasons and methods to promote the performance.This paper mainly based on the AdaBoost detection algorithm. By analyzing AdaBoost training and detecting process, this paper points out the essentials that affect the speed of detection, and put forward through regional growth, corrosion, and other pretreatment methods to reduce the complexity of the background to enhance the speed of detection. In addition, the paper trained the profiles to get cascade classifiers as an complementarity of the system, so to expand the scale of human face detection.The tracking algorithm used is mainly based on particle filter algorithm. By researches on the elements affecting particle filter tracking speed and instability, this paper designed the adaptive control of particles dispersion mechanisms and a new search model, redefined the way of feature extraction, finally enhanced the speed and the accuracy of tracking system.This paper combined detection and tracking, used the tracking results as feedback and reset regions of interest, thereby to check faces and define the exact location. By this way, the system reduces false alarm rate. Meanwhile, this paper provided a model of the human eyes to check the faces, showed the advantages and limitations of this method.The research was carried out on PC platform with the software development kits developed by Intel named OpenCV, which is used for digital video and image processing. The result shows that this system is fast and precise, basically mean the requirement of real-time surveillance.