Research On Moving Object Detection And Tracking In Complex Condition

Moving target detection and tracking is one of the most important subjects in computer vision. It has important and practical value together with wide developmental prosperity in video surveillance, security defense, and military hardware and so on. After years of joint efforts made by domestic and foreign researchers, this technology has been developed rapidly and achieved a lot of significant results. However, there are new challenges existing in practice complex environment, illumination changes, target occlusion, shadow interference, the impact of the presence of deformation and so on. To solve these problems and compensate the practical need of the requirements, it is necessary to design new algorithm of real-time performance and high robustness. This paper deeply studied many core algorithms and did new trial based on those. The main works in this paper are as follows:1. By deeply investigating many classical methods of moving target detection and tracking, this paper points out their advantages, disadvantages and the appropriate application conditions of them. This paper proposes an algorithm based on multi-featured modeling, Mean Shift and Particle Filter combining. Object modeling combines the color, edge, texture and other general features. Using the concept of the structural similarity (SSIM) in the field of image quality evaluation for reference, this paper extracts the structural information of the target as the fourth sub-feature. Also,the paper improves the multi-feature fusion method to get a higher robustness target model. Finally this paper track objects in the improved MSPF (Mean Shift & Particle Filter) tracking framework and test the algorithm by numerous experiments.2. The paper presents an object extraction algorithm based on line scanning. This algorithm scans the image line by line to extract object, other than searching object in whole area of images. In embedded DSP development, this algorithm is combined with DMA and ping-pong buffer, which could greatly improve the speed of the operation.3. This paper transplants the multi-feature fusion MSPF tracking algorithm to ADSP BF561 development platform. Many problems in migration are Solve including image format conversion and so on. The line-scanning object extraction algorithm is used to optimize this tracking algorithm in the implementation on this platform. C-level and assembly-level code optimization are also included in to further improve the efficiency to meet the real-time requirement of the target tracking application.