Solution for Point Tracking Task of ECCV 2nd Perception Test Challenge 2024

TL;DR

This paper presents FPD, a novel method for improving point tracking in videos, especially for static points, by perceiving and correcting trajectories at multiple granularities, achieving high performance in ECCV 2024 challenge.

Contribution

Introduction of FPD, a simple yet effective approach for multi-granularity point perception and trajectory correction in point tracking tasks.

Findings

Achieved second highest score in ECCV 2024 challenge.

Effective static point detection and trajectory correction.

Improved performance in long-sequence video tracking.

Abstract

This report introduces an improved method for the Tracking Any Point~(TAP), focusing on monitoring physical surfaces in video footage. Despite their success with short-sequence scenarios, TAP methods still face performance degradation and resource overhead in long-sequence situations. To address these issues, we propose a simple yet effective approach called Fine-grained Point Discrimination~(\textbf{FPD}), which focuses on perceiving and rectifying point tracking at multiple granularities in zero-shot manner, especially for static points in the videos shot by a static camera. The proposed FPD contains two key components: $(1)$ Multi-granularity point perception, which can detect static sequences in video and points. $(2)$ Dynamic trajectory correction, which replaces point trajectories based on the type of tracked point. Our approach achieved the second highest score in the final test with a score of $0.4720$.