Feature Point-Based Object Tracking

Abstract

In this paper, we describe a new algorithm for tracking a known static target in image frames from a moving platform. We have noticed that a image point is environment sensitive, but those changes of grouped points energy have their own statistical similarities in two image frames within limited time interval. This approach analyzes correspondence of interest points around every feature points between inter-frames in image sequence in order to decide those feature points. We tackle these tasks with three broad approaches. First, we make an active contour model of a target in order to build some low-energy feature points. The feature points give constraints of the input state space for interest point detection in an input image frame. The second step is a method of detecting interest points around every kernel. We take into account auto-correlation method to indicate the presence of interest points for the purpose of features state space in consecutive image frames that can be tracked. The third step of our strategy is to detect the correspondence of interest points by a probabilistic relaxation method in tracking windows. The detecting process is iterative and begins with the detection of all potential correspondence pair in consecutive image. Each pair of corresponding points is then iteratively recomputed to get a globally optimum set of pairwise correspondences. Successful results are given for a real vide frames.