Learning a Robust Society of Tracking Parts using Co-occurrence Constraints

TL;DR

This paper introduces a society of tracking parts using co-occurrence constraints within a deep neural network to improve robustness and adaptability in object tracking, achieving state-of-the-art results on VOT17.

Contribution

It proposes a novel deep network with two pathways, combining conservative and online learning, guided by co-occurrence constraints for robust and adaptive tracking.

Findings

Achieves state-of-the-art performance on VOT17 benchmark.

Outperforms existing methods on EAO metric and number of fails.

Demonstrates robustness and adaptability in challenging tracking scenarios.

Abstract

Object tracking is an essential problem in computer vision that has been researched for several decades. One of the main challenges in tracking is to adapt to object appearance changes over time and avoiding drifting to background clutter. We address this challenge by proposing a deep neural network composed of different parts, which functions as a society of tracking parts. They work in conjunction according to a certain policy and learn from each other in a robust manner, using co-occurrence constraints that ensure robust inference and learning. From a structural point of view, our network is composed of two main pathways. One pathway is more conservative. It carefully monitors a large set of simple tracker parts learned as linear filters over deep feature activation maps. It assigns the parts different roles. It promotes the reliable ones and removes the inconsistent ones. We learn these filters simultaneously in an efficient way, with a single closed-form formulation, for which we propose novel theoretical properties. The second pathway is more progressive. It is learned completely online and thus it is able to better model object appearance changes. In order to adapt in a robust manner, it is learned only on highly confident frames, which are decided using co-occurrences with the first pathway. Thus, our system has the full benefit of two main approaches in tracking. The larger set of simpler filter parts offers robustness, while the full deep network learned online provides adaptability to change. As shown in the experimental section, our approach achieves state of the art performance on the challenging VOT17 benchmark, outperforming the published methods both on the general EAO metric and in the number of fails, by a significant margin.