Attention to Trajectory: Trajectory-Aware Open-Vocabulary Tracking

• URL: http://arxiv.org/abs/2503.08145v1
• Date: Tue, 11 Mar 2025 08:03:47 GMT
• Title: Attention to Trajectory: Trajectory-Aware Open-Vocabulary Tracking
• Authors: Yunhao Li, Yifan Jiao, Dan Meng, Heng Fan, Libo Zhang,
• Abstract summary: OV-MOT aims to enable approaches to track objects without being limited to a predefined set of categories.<n>We propose textbfTRACT, an open-vocabulary tracker that leverages trajectory information to improve both object association and classification in OV-MOT.
• Score: 23.65057966356924
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Open-Vocabulary Multi-Object Tracking (OV-MOT) aims to enable approaches to track objects without being limited to a predefined set of categories. Current OV-MOT methods typically rely primarily on instance-level detection and association, often overlooking trajectory information that is unique and essential for object tracking tasks. Utilizing trajectory information can enhance association stability and classification accuracy, especially in cases of occlusion and category ambiguity, thereby improving adaptability to novel classes. Thus motivated, in this paper we propose \textbf{TRACT}, an open-vocabulary tracker that leverages trajectory information to improve both object association and classification in OV-MOT. Specifically, we introduce a \textit{Trajectory Consistency Reinforcement} (\textbf{TCR}) strategy, that benefits tracking performance by improving target identity and category consistency. In addition, we present \textbf{TraCLIP}, a plug-and-play trajectory classification module. It integrates \textit{Trajectory Feature Aggregation} (\textbf{TFA}) and \textit{Trajectory Semantic Enrichment} (\textbf{TSE}) strategies to fully leverage trajectory information from visual and language perspectives for enhancing the classification results. Extensive experiments on OV-TAO show that our TRACT significantly improves tracking performance, highlighting trajectory information as a valuable asset for OV-MOT. Code will be released.

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