An Adaptive Approach for Validation in Visual Object Tracking

Abstract

Abstract One of the major goals in the field of computer vision is to enable computers to replicate the basic functions of human vision such as motion perception and scene understanding. To achieve the goal of intelligent motion perception, much effort has been spent on visual object tracking. Research interest in visual object tracking comes from the fact that it has a wide range of real-world applications. The uncertainty of validating unpredictable features in object tracking is a challenging task in visual object tracking with occlusion and large appearance variation. To address this uncertainty, we propose an adaptive approach which uses updating model based on the occlusion and distortion parameters. In case of occlusion or large appearance variation, the proposed method uses backward model validation where it updates the invalid appearance and then validates the target feature model. If the target feature did not undergo any kind of clutter or distortions, it simply validates and then updates the appearance model using forward feature validation. The experimental results obtained from this adaptive approach demonstrate effectiveness in terms of OR (Overlap Rate) and Center Location Error, compared with other relevant existing algorithms.