Adaptive patch feature matching and scale estimation for visual object tracking

Abstract

In computer vision, the object tracking technique is a complex task to predict the object target under challenging task occlusion, scaling, and illumination. A robust object tracking algorithm is proposed based on adaptive patch feature matching and scale estimation. Initially, tracking technique extracts geometric locations using defined directional distances and object location of the previous frame. The feature matching is carried out between the previous frame featured vector and overlapping patches feature vectors of the present frame. The matched patch is bounded by the identified location in the current frame. For object scaling, speeded up robust features point matching technique is applied to an enlarged patch of the present frame and the tracked patch of the previous frame. Finally, the proposed algorithm updates motion, feature, and geometric location vectors to keep tracking between successive frames. The feature vector update between successive frames solves the aforementioned challenging issues in object tracking. For experimentation, the proposed algorithm is tested with available tracking benchmarks. The performance evaluation results show that the proposed technique is better in terms of computational time and accuracy compared to conventional tracking techniques.