Visual Tracking via Boolean Map Representations

TL;DR

This paper introduces a Boolean map-based representation for visual tracking that encodes multi-scale connectivity cues, improving accuracy and robustness while maintaining computational efficiency.

Contribution

It proposes a novel Boolean map representation that captures multi-scale connectivity cues for more effective visual tracking.

Findings

Achieves superior accuracy and robustness compared to state-of-the-art methods.

Computationally efficient due to explicit feature map approximation.

Performs well on a large benchmark dataset of 50 sequences.

Abstract

In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking. We describe a target object with histogram of oriented gradients and raw color features, of which each one is characterized by a set of Boolean maps generated by uniformly thresholding their values. The Boolean maps effectively encode multi-scale connectivity cues of the target with different granularities. The fine-grained Boolean maps capture spatially structural details that are effective for precise target localization while the coarse-grained ones encode global shape information that are robust to large target appearance variations. Finally, all the Boolean maps form together a robust representation that can be approximated by an explicit feature map of the intersection kernel, which is fed into a logistic regression classifier with online update, and the target location is estimated within a particle filter framework. The proposed representation scheme is computationally efficient and facilitates achieving favorable performance in terms of accuracy and robustness against the state-of-the-art tracking methods on a large benchmark dataset of 50 image sequences.