Dynamic feature fusion with spatial-temporal context for robust object tracking

Abstract

Feature fusion has been widely used for improving the tracking performance. However, how to effectively analyze the characteristics of different visual features to realize dynamical feature fusion is still a challenging task. In this paper, we propose a spatial-temporal context-based dynamic feature fusion method (STCDFF) with the correlation filters framework for object tracking. The proposed STCDFF method exploits spatial-temporal context to deeply analyze the characteristics of multiple visual features (e.g., HOG, Color-Names and CNN features) to perform feature fusion. On the one hand, spatial context is employed to evaluate the discriminative ability of different features to distinguish the target object from the background. On the other hand, temporal context is utilized to consider the representative ability of different features to capture significant appearance changes of the target object. The weight of a feature is decided by both its discriminative ability and representative ability. By exploring spatial-temporal context for feature fusion, the STCDFF method can fully utilize the strengths of different features to handle complex appearance changes and background clutters to achieve better performance. Extensive experiments on multiple object tracking datasets prove that our STCDFF method performs competitively against several popular tracking methods.