Research On Content-Based Surveillance Video Retrieval System

With the development of video surveillance equipment and the computer vision,video surveillance has been widely used in the security prevention,traffic control,criminal investigation and other fields.On the one hand is the world is facing a serious anti-terrorism situation,on the other hand is the people's growing awareness of security,in such a dual context,video surveillance is playing an increasingly important role.But the video data contains a very large amount of information,it's a big problem to processing the massive video data accurately and efficiently.In the field of public security criminal investigation,the current way of the suspects tracking in the video mostly rely on manpower to complete,its efficiency is very low.And the existing surveillance video retrieval method has a bad performance in the efficiency and accuracy.In view of the above problems,we urgently need new technical means to solve,hoping to achieve more accurate and faster in the surveillance video to find relevant targets.In order to solve these problems,this paper makes a deep research on the key technology of content-based surveillance video retrieval.Specifically,the main research work of this paper is as follows:(1)Aiming at the problem that the accuracy and efficiency of the traditional key frame and key frame ROI extraction algorithm are generally not high,this paper presents a key frame and ROI extraction algorithm based on human detection.By introducing human detection technology,this algorithm realizes more accurate selection of key frames and more accurate calibration of ROI regions.Experiments show that the method has a better effect.(2)The existing algorithm's detection effect of human body detection in complex scene is not ideal.In order to improve the detection effect of human in complex scenes,this paper presents a multi-part optimal feature combination of human detection method to improve the accuracy of human detection in complex scenes.(3)In this paper,we focus on the key frame retrieval matching algorithm.In view of the shortcomings of the traditional SIFT algorithm,this paper adopts the supervised LPP algorithm and the approximate nearest neighbor matching algorithm based on the vector angle to reduce the dimension and accelerate the SIFT feature's matching,and effectively improve the SIFT feature point matching speed.In order to improve the accuracy of retrieval,this paper presents an improved SIFT algorithm that combines color features.The experimental results show that the algorithm has higher retrieval accuracy.(4)This paper designs and implements a surveillance video retrieval cloud platform,including the construction of cloud platform,image processing module and database module development and deployment work.The platform can provide stability,colleges and universities,accurate retrieval services,through the performance test,verify the effectiveness and practicality of the platform.