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arXiv · 2511.03332

Multi-Object Tracking Retrieval with LLaVA-Video: A Training-Free Solution to MOT25-StAG Challenge

Yi Yang·Yiming Xu·Timo Kaiser·Hao Cheng·Bodo Rosenhahn·Michael Ying Yang
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Abstract

In this report, we present our solution to the MOT25-Spatiotemporal Action Grounding (MOT25-StAG) Challenge. The aim of this challenge is to accurately localize and track multiple objects that match specific and free-form language queries, using video data of complex real-world scenes as input. We model the underlying task as a video retrieval problem and present a two-stage, zero-shot approach, combining the advantages of the SOTA tracking model FastTracker and Multi-modal Large Language Model LLaVA-Video. On the MOT25-StAG test set, our method achieves m-HIoU and HOTA scores of 20.68 and 10.73 respectively, which won second place in the challenge.

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Yi Yang, Yiming Xu, Timo Kaiser, Hao Cheng, Bodo Rosenhahn, Michael Ying Yang. 2025-11-05. Multi-Object Tracking Retrieval with LLaVA-Video: A Training-Free Solution to MOT25-StAG Challenge. https://arxiv.org/abs/2511.03332

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