Establishing Object Correspondences by Utilizing Surrounding Information

Tracking objects in motion is often done by imposing the constraints of kinematics and local image properties onto the objects. In this work, we propose a novel tracking algorithm which uses the surrounding information of the object to construct the feature profiles. The object feature profiles are then compared across consecutive frames to locate the targets. The feature profiles possess two important properties, distinctive-ness and coherence, which make them robust to measurement noises, short occlusions and false targets. The matching cost function is formulated under a Bayesian framework that enables the algorithm to capture the properties in the form of probabilities. The algorithm is also self-initializing. The computation of the feature profiles is fast due to their simple definition; and the comparison between two profiles can also be done efficiently.