Flow Guided Short-term Trackers with Cascade Detection for Long-term Tracking

TL;DR

This paper introduces a novel long-term object tracking algorithm that combines flow-guided short-term tracking with cascade detection, effectively handling target disappearance and reappearance in long sequences.

Contribution

It proposes a new long-term tracking method, flow_MDNet_RPN, integrating tracking judgment and detection modules into existing short-term trackers.

Findings

Effective in handling target disappearance and reappearance.

Improves long-term tracking accuracy.

Demonstrates robustness in occlusion scenarios.

Abstract

Object tracking has been studied for decades, but most of the existing works are focused on the short-term tracking. For a long sequence, the object is often fully occluded or out of view for a long time, and existing short-term object tracking algorithms often lose the target, and it is difficult to re-catch the target even if it reappears again. In this paper a novel long-term object tracking algorithm flow_MDNet_RPN is proposed, in which a tracking result judgement module and a detection module are added to the short-term object tracking algorithm. Experiments show that the proposed long-term tracking algorithm is effective to the problem of target disappearance.