A moving object tracking method based on sequential detection scheme

A single method for object tracking has low accuracy in a complex environment. To overcome this limitation, a novel object tracking method is studied in this paper, based on sequential detection scheme. First, particle filter object tracking approach with color feature was applied to estimate optimal tracking window, and sparse field active contours is performed or not based on the similarity between object and resulting window, and then the similarity between object and the obtained contour is calculated in sequence to determine whether Camshift should be employed to modify the object contour. And thus the accuracy of moving object tracking will be greatly improved. Experiments demonstrated that compared with the classic particles filter object tracking approach with color feature and Camshift algorithm, the proposed algorithm can track the moving target successfully in different scenarios and it can handle target with scale, orientation, and view changes; moreover, it has better robustness and accuracy of object tracking.