Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking

TL;DR

Hybrid-SORT enhances online multi-object tracking by integrating weak cues like confidence and height with traditional strong cues, significantly improving performance especially in occlusion and clustering scenarios while maintaining real-time capabilities.

Contribution

The paper introduces a novel multi-object tracking method that incorporates weak cues such as confidence and height, improving robustness without sacrificing speed or requiring training.

Findings

Significant improvements on MOT benchmarks.

Effective in occlusion and clustering scenarios.

Compatible with various trackers and scenarios.

Abstract

Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), which exhibit powerful instance-level discrimination. However, when object occlusion and clustering occur, spatial and appearance information will become ambiguous simultaneously due to the high overlap among objects. In this paper, we demonstrate this long-standing challenge in MOT can be efficiently and effectively resolved by incorporating weak cues to compensate for strong cues. Along with velocity direction, we introduce the confidence and height state as potential weak cues. With superior performance, our method still maintains Simple, Online and Real-Time (SORT) characteristics. Also, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner. Significant and consistent improvements are observed when applying our method to 5 different representative trackers. Further, with both strong and weak cues, our method Hybrid-SORT achieves superior performance on diverse benchmarks, including MOT17, MOT20, and especially DanceTrack where interaction and severe occlusion frequently happen with complex motions. The code and models are available at https://github.com/ymzis69/HybridSORT.