Multi-Granularity Language-Guided Training for Multi-Object Tracking

TL;DR

This paper introduces LG-MOT, a multi-object tracking framework that integrates multi-granularity language information with visual features to improve robustness and achieve state-of-the-art results across multiple benchmarks.

Contribution

The work proposes a novel multi-modal approach that leverages scene- and instance-level language descriptions to enhance visual feature discrimination in multi-object tracking.

Findings

Achieves state-of-the-art performance on MOT17, DanceTrack, and SportsMOT datasets.

Improves target association accuracy by 2.2% IDF1 score on DanceTrack.

Demonstrates strong cross-domain generalizability of the proposed method.

Abstract

Most existing multi-object tracking methods typically learn visual tracking features via maximizing dis-similarities of different instances and minimizing similarities of the same instance. While such a feature learning scheme achieves promising performance, learning discriminative features solely based on visual information is challenging especially in case of environmental interference such as occlusion, blur and domain variance. In this work, we argue that multi-modal language-driven features provide complementary information to classical visual features, thereby aiding in improving the robustness to such environmental interference. To this end, we propose a new multi-object tracking framework, named LG-MOT, that explicitly leverages language information at different levels of granularity (scene-and instance-level) and combines it with standard visual features to obtain discriminative representations. To develop LG-MOT, we annotate existing MOT datasets with scene-and instance-level language descriptions. We then encode both instance-and scene-level language information into high-dimensional embeddings, which are utilized to guide the visual features during training. At inference, our LG-MOT uses the standard visual features without relying on annotated language descriptions. Extensive experiments on three benchmarks, MOT17, DanceTrack and SportsMOT, reveal the merits of the proposed contributions leading to state-of-the-art performance. On the DanceTrack test set, our LG-MOT achieves an absolute gain of 2.2\% in terms of target object association (IDF1 score), compared to the baseline using only visual features. Further, our LG-MOT exhibits strong cross-domain generalizability. The dataset and code will be available at https://github.com/WesLee88524/LG-MOT.