Jinwen Yu

Pan Liao, Feng Yang, Di Wu et al.

Multi-Object Tracking (MOT) is evolving from geometric localization to Semantic MOT (SMOT) to answer complex relational queries, yet progress is hindered by semantic data scarcity and a structural disconnect between tracking architectures and Multi-modal Large Language Models (MLLMs). To address this, we introduce Grand-SMOT, a large-scale, open-world benchmark providing high-density, dual-stream narratives that comprehensively decouple individual behaviors from environmental contexts. Furthermore, we propose LLMTrack, the first framework to seamlessly integrate MLLMs into the SMOT task. LLMTrack establishes a Macro-Understanding-First paradigm, utilizing a novel Spatio-Temporal Fusion Module to align discrete geometric trajectories with continuous semantic features, effectively suppressing temporal hallucinations during online processing. Extensive experiments demonstrate that LLMTrack achieves state-of-the-art geometric tracking performance while delivering a qualitative leap in dynamic semantic reasoning. Notably, our analysis reveals that high-quality semantic narratives empower the language model to deduce complex social interactions naturally, demonstrating that direct cognitive reasoning is more effective than cumbersome explicit visual modeling. Ultimately, our contributions bridge the gap between perceptual tracking and cognitive reasoning, establishing a robust new foundation for comprehensive video understanding and intelligent narrative generation.

Pan Liao, Feng Yang, Di Wu et al.

Transformer-based multi-object tracking (MOT) methods have captured the attention of many researchers in recent years. However, these models often suffer from slow inference speeds due to their structure or other issues. To address this problem, we revisited the Joint Detection and Tracking (JDT) method by looking back at past approaches. By integrating the original JDT approach with some advanced theories, this paper employs an efficient method of information transfer between frames on the DETR, constructing a fast and novel JDT-type MOT framework: FastTrackTr. Thanks to the superiority of this information transfer method, our approach not only reduces the number of queries required during tracking but also avoids the excessive introduction of network structures, ensuring model simplicity. Experimental results indicate that our method has the potential to achieve real-time tracking and exhibits competitive tracking accuracy across multiple datasets.