EPIPTrack: Rethinking Prompt Modeling with Explicit and Implicit Prompts for Multi-Object Tracking

• URL: http://arxiv.org/abs/2510.13235v1
• Date: Wed, 15 Oct 2025 07:39:30 GMT
• Title: EPIPTrack: Rethinking Prompt Modeling with Explicit and Implicit Prompts for Multi-Object Tracking
• Authors: Yukuan Zhang, Jiarui Zhao, Shangqing Nie, Jin Kuang, Shengsheng Wang,
• Abstract summary: We propose a unified vision-language tracking framework, named EPIPTrack.<n>EPIPTrack leverages explicit and implicit prompts for dynamic target modeling and semantic alignment.<n>Experiments on MOT17, MOT20, and Dance demonstrate that EPIPTrack outperforms existing trackers in diverse scenarios.
• Score: 10.065921746316642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal semantic cues, such as textual descriptions, have shown strong potential in enhancing target perception for tracking. However, existing methods rely on static textual descriptions from large language models, which lack adaptability to real-time target state changes and prone to hallucinations. To address these challenges, we propose a unified multimodal vision-language tracking framework, named EPIPTrack, which leverages explicit and implicit prompts for dynamic target modeling and semantic alignment. Specifically, explicit prompts transform spatial motion information into natural language descriptions to provide spatiotemporal guidance. Implicit prompts combine pseudo-words with learnable descriptors to construct individualized knowledge representations capturing appearance attributes. Both prompts undergo dynamic adjustment via the CLIP text encoder to respond to changes in target state. Furthermore, we design a Discriminative Feature Augmentor to enhance visual and cross-modal representations. Extensive experiments on MOT17, MOT20, and DanceTrack demonstrate that EPIPTrack outperforms existing trackers in diverse scenarios, exhibiting robust adaptability and superior performance.

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