Research And Application Of Face Detection And Tracking Algorithm Based On Video Surveillance

In recent years,the popularity of video surveillance system and the scale of video surveillance are growing,which have outstanding results for security,abnormal alarm,criminal evidence and detection of criminal cases.The Face detection and tracking is not only a critical technology in video surveillance system,but also a popular research field in computer vision field,which has important research value and broad market prospect.As the video surveillance environment is complex,which makes face detection and tracking difficult.And the existing face detection algorithm has low detection efficiency and accuracy in complex environment,the existing face tracking algorithm has poor performance of real-time,robustness and tracking.In this paper,the face detection and tracking algorithm in video surveillance is researched,which has great significance to improve the face detection efficiency,accuracy and real-time and robustness in face tracking.In light of the above problems,this paper proposes an Adaboost face detection algorithm based on skin color segmentation filtering and a particle filter face tracking algorithm based on Camshift clustering,which are applied to the client of video surveillance to achieve the face detection and tracking.The main works of this paper are as follows:1.According to the problem of improving the efficiency and accuracy of face detection in video surveillance,we propose an Adaboost face detection algorithm based on skin color segmentation and filtering.After selecting the color space and skin color model,we make the first detection by skin color segmentation and filtering to select a similar face area.Then through the Adaboost algorithm to train the face set,the face area is fed into the cascade classifier of the Adaboost algorithm and detect the face again.Thus the cascade classifier of Adaboost algorithm does not need to search and detect the whole picture which is to be detected,just need to detect the similar face area,which improves the efficiency and the accuracy of face detection.The experimental results show the good improvement of detection rate and false detection rate of the proposed algorithm.2.Aiming at resolving the problems that the Camshift algorithm cannot deal with similar background color interference,background complexity and traditional particle filter face tracking algorithm has particle degradation and high computation,a particle filter face tracking algorithm based on Camshift clustering is proposed.In the framework of particle filter,the clustering method in Camshift algorithm is introduced into the face statement estimation,so that each particle is iterated along the direction of maximal gradient to the region of maximal local density and all the particles move to the area that has similar face color.The experimental results show that the proposed algorithm achieves the face tracking under irregular curve motion with fewer particles and good robustness in illumination and occlusion.Compared with traditional particle filter face tracking algorithm,the proposed algorithm has better performances in real time,robustness and effectiveness of face tracking.3.The face detection and tracking algorithm proposed in this paper is applied to the Directshow framework,and implements the face detection and tracking function in the video surveillance client.Tests demonstrate that the proposed algorithm has high detection efficiency and accuracy in face detection and meets the application requirements of real-time processing in face tracking,which is significant for the follow-up face information processing and application of in video surveillance.