Research On Algorithm And Application Of Visual Object Tracking Under Complex Envirionment

As one of the crucial issues of computer vision, visual object tracking is widely used in many applications, such as visual surveillance, human-computer interaction, visual transportation, visual navigation of robots, military guidance, etc. Along with the rapid development of information techniques, more and more researchers devoted themselves to the research area of visual object tracking, and many effective algorithms have been proposed, some of which have great performance under certain environment. However, there are still many difficulties to the research and application of robust algorithm, due to the complex environment, such as complex background, change of the appearance, and occlusion, etc.This dissertation includes two parts: firstly, the research of robust tracking algorithm under complex environment, and secondly, the realization of tracking algorithm based on hardware platform. The main contributions of the dissertation are summarized as follows:(1) Tracking algorithm based on Mean-shift is deeply discussed. Due to the background pixels, the traditional Mean-shift algorithm can not locate the object exactly. Improved Mean-shift algorithm based on the most discriminative grey level features is proposed. According to the difference of grey distribution between the object and the background in the initial frame, log-likelihood image is set up to select the discriminative grey level features for object modeling. The candidate modeling is done the same way in the next frames. The improved Mean-shift algorithm may not only reduce the impact of the background pixels to object localization and increase the precision of localization, but also reduce the iteration times of the algorithm, and increase the speed of computation.(2) Centroid weighted algorithm is proposed and improved in this dissertation. The ultimate location of the object is the expectation of the centroids of the pixels of the same grey level in the tracking area. The centroid weighted algorithm has three advantages. Firstly, the algorithm includes spatial information of the color distribution besides the statistical information, which makes it more precise. Secondly, it is very simple and needs only one step computation without iterations, which makes it very suitable for real-time application. Thirdly, rather than the improved Mean-shift algorithm, the improved centroid weighted algorithm is more robust, when partial occlusion happens. On the other hand, the model updating strategy is proposed, which makes the tracking algorithm more robust.(3)Occlusion problem is deeply discussed in the dissertation and integrated algorithm of object tracking is proposed. Bhattacharyya coefficient is proposed, which is very sensitive to occlusion. Kalman filter is the main framework of tracking. According to the degree of occlusion, different strategies are used. The proposed centroid weighted algorithm is robust to partial occlusion, so no special treatment is needed to partial occlusion. Fragments based algorithm is used when serious occlusion happens. When totally occluded, the predicted location of the Kalman filter is chosen as the object location. The strategy of dealing with occlusion is robust to partial occlusion, serious occlusion and total occlusion.(4)Video tracker is developed based on the framework of"DSP+FPGA". Three problems of the hardware are resolved, and they are object tracking in low temperature, the configuration of FPGA and EMC of the hardware. The code of DSP is written and optimized, and robust tracking under variable light condition is realized. The video tracker has now already passed the environment test, and the performance of both hardware and software all meet with the requirements, such as stability, reliability and real-time, etc.