Research And Implementation Of Moving Object Detection And Tracking System Based On DSP

Moving target detection and tracking is one of the most important subjects in computer vision. This paper deeply studied many core algorithms and focus on the research of moving target tracking system based on DSP,involving background subtraction detection, mean shift and particle filter and other related algorithms and migration on the DSP, in line with the trend moving target detection and tracking technology to the development of intelligent miniaturized.This paper select the the nuclear density background modeling as the moving target detection methods. The binary image has been generated by OTSU adaptive threshold algorithm. In order to obtain the accurate image of the moving target,morphological filter has also been used as the image post-processing methods.Then 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. At the same time,the paper improves the multi- feature fusion method to get a higher robustness target model. Finally this paper track objects in the MSPF(Mean Shift & Particle Filter) tracking framework and test the algorithm by numerous experiments.This paper transplants the multi- feature fusion MSPF tracking algorithm to DSP based on the hardware platform of ADSP-BF561. Many problems in migration are solved including image format conversion and so on. 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.Some tests based on the moving target detection and tracking system in this paper have been done in the indoor environment, and the result shows that it has a good performance of real-time and robustness.