Hide and Seek tracker: Real-time recovery from target loss

TL;DR

This paper introduces a real-time recovery method for video trackers that detects low-confidence tracking failures and swiftly updates the target position using visual content, improving robustness without extra computational cost.

Contribution

It proposes a novel real-time recovery mechanism based on confidence estimation and visual content analysis, enhancing tracker robustness during target loss.

Findings

Successful recovery from target loss in real-time

Maintains tracking accuracy on standard datasets

No significant computational overhead introduced

Abstract

In this paper, we examine the real-time recovery of a video tracker from a target loss, using information that is already available from the original tracker and without a significant computational overhead. More specifically, before using the tracker output to update the target position we estimate the detection confidence. In the case of a low confidence, the position update is rejected and the tracker passes to a single-frame failure mode, during which the patch low-level visual content is used to swiftly update the object position, before recovering from the target loss in the next frame. Orthogonally to this improvement, we further enhance the running average method used for creating the query model in tracking-through-similarity. The experimental evidence provided by evaluation on standard tracking datasets (OTB-50, OTB-100 and OTB-2013) validate that target recovery can be successfully achieved without compromising the real-time update of the target position.