The Second-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge

TL;DR

This paper presents a high-ranking solution for multi-person tracking in group dance videos, combining a novel online tracker with offline refinement to improve accuracy amidst irregular motions.

Contribution

The paper introduces the Cascaded Buffer-IoU (C-BIoU) tracker, a novel online tracking method that enhances matching robustness by expanding the search space and employing cascaded matching.

Findings

Achieved second place in ECCV 2022 Group Dance Challenge.

C-BIoU tracker improves matching accuracy in irregular motion scenarios.

Offline refinement further enhances tracking performance.

Abstract

This is our 2nd-place solution for the ECCV 2022 Multiple People Tracking in Group Dance Challenge. Our method mainly includes two steps: online short-term tracking using our Cascaded Buffer-IoU (C-BIoU) Tracker, and, offline long-term tracking using appearance feature and hierarchical clustering. Our C-BIoU tracker adds buffers to expand the matching space of detections and tracks, which mitigates the effect of irregular motions in two aspects: one is to directly match identical but non-overlapping detections and tracks in adjacent frames, and the other is to compensate for the motion estimation bias in the matching space. In addition, to reduce the risk of overexpansion of the matching space, cascaded matching is employed: first matching alive tracks and detections with a small buffer, and then matching unmatched tracks and detections with a large buffer. After using our C-BIoU for online tracking, we applied the offline refinement introduced by ReMOTS.