Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation

• URL: http://arxiv.org/abs/2212.01568v1
• Date: Sat, 3 Dec 2022 07:57:31 GMT
• Title: Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation
• Authors: En Yu, Songtao Liu, Zhuoling Li, Jinrong Yang, Zeming li, Shoudong Han, Wenbing Tao
• Abstract summary: We propose to introduce natural language representation into visual MOT models for boosting the domain generalization ability. To tackle this problem, we design two modules, namely visual context prompting (VCP) and visual-language mixing (VLM) VLM joints the information in the generated visual prompts and the textual prompts from a pre-defined Trackbook to obtain instance-level pseudo textual description. Through training models on MOT17 and validating them on MOT20, we observe that the pseudo textual descriptions generated by our proposed modules improve the generalization performance of query-based trackers by large margins.
• Score: 33.03600813115465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although existing multi-object tracking (MOT) algorithms have obtained competitive performance on various benchmarks, almost all of them train and validate models on the same domain. The domain generalization problem of MOT is hardly studied. To bridge this gap, we first draw the observation that the high-level information contained in natural language is domain invariant to different tracking domains. Based on this observation, we propose to introduce natural language representation into visual MOT models for boosting the domain generalization ability. However, it is infeasible to label every tracking target with a textual description. To tackle this problem, we design two modules, namely visual context prompting (VCP) and visual-language mixing (VLM). Specifically, VCP generates visual prompts based on the input frames. VLM joints the information in the generated visual prompts and the textual prompts from a pre-defined Trackbook to obtain instance-level pseudo textual description, which is domain invariant to different tracking scenes. Through training models on MOT17 and validating them on MOT20, we observe that the pseudo textual descriptions generated by our proposed modules improve the generalization performance of query-based trackers by large margins.

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