Using a Non-prior Training Active Feature Model

This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT- AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The pro- posed object tracking procedure can be divided into two steps: (i) opti- cal flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The pro- posed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time.