The Research And Implementation Of Visual Tracking Algorithm For Simulating Human Visual Mechanism

The visual tracking is defined as a real-time system to locate the target continuously from the image sequences, in accordance with the given target model. As one of the most important computer vision tasks, it is widely used in many areas, including smart rooms, perceptual user interfaces, vehicle navigation and intelligent monitoring, etc.Although various work on visual tracking algorithm have been taken, a visual tracking system which can simulate human visual mechanism and achieve comparable to performance of human vision is still a challenging and promising subject. In this paper, two novel tracking algorithms have been proposed from a biological standpoint. The first algorithm, based on compressive sensing theories, applies sparse representation in the frame of the Lucas-Kanade image registration algorithm. The target appearance is represented by the sparse linear combination of the overcomplete dictionary. The target state parameters are solved to realized precise tracking by the minimizing the 7)1-norm of the alignment error. The dictionary is updated dynamically with the tracking result in each frames. To deal with tracking drift caused by dictionary update, a two-stage iteration mechanism is adopted. At the first stage, the target is searched with the dynamic dictionary, while the next search starts from the result of the first stage with the static template. The Matlab simulation experiments show the proposed algorithm is fast and robust to the long video.Despite very good performance, the first algorithm cannot work like human which searches the target by the feature match and object recognition. The second algorithm, named the BIOT, use the biologically inspired model(BIM) to represent the target. A complete BIM can simulate the visual cortex mechanism, including the primary visual cortex and the advanced visual cortex. The features of prototype patches from the primary visual cortex are used to search the target; the support vector machine(SVM) classifier simulates the advanced visual cortex to recognize target from the background. More, during target searching, an iterative process which could dynamically update the candidate set and greatly decrease the number of examined targets is present in BIOT to overcome the huge time cost caused by the BIM feature extraction. Numerous contrast experimental results validate that the proposed method is effective and robust to illumination changes, partial occlusions and some appearance changes, and superior to others.