Yihao Zhen

Yihao Zhen, Mingyue Xu, Qiang Wang et al.

Multi-Camera Multi-Target (MCMT) tracking aims to locate and associate the same targets across multiple camera views. Existing methods typically adopt a two-stage framework, involving single-camera tracking followed by inter-camera tracking. However, in this paradigm, multi-view information is used only to recover missed matches in the first stage, providing a limited contribution to overall tracking. To address this issue, we propose GMT, a global MCMT tracking framework that jointly exploits intra-view and inter-view cues for tracking. Specifically, instead of assigning trajectories independently for each view, we integrate the same historical targets across different views as global trajectories, thereby reformulating the two-stage tracking as a unified global-level trajectory-target association process. We introduce a Cross-View Feature Consistency Enhancement (CFCE) module to align visual and spatial features across views, providing a consistent feature space for global trajectory modeling. With these aligned features, the Global Trajectory Association (GTA) module associates new detections with existing global trajectories, enabling direct use of multi-view information. Compared to the two-stage framework, GMT achieves significant improvements on existing datasets, with gains of up to 21.3 percent in CVMA and 17.2 percent in CVIDF1. Furthermore, we introduce VisionTrack, a high-quality, large-scale MCMT dataset providing significantly greater diversity than existing datasets. Our code and dataset will be released.

Yihao Zhen, Qiang Wang, Yu Qiao et al.

A main challenge of Visual-Language Tracking (VLT) is the misalignment between visual inputs and language descriptions caused by target movement. Previous trackers have explored many effective feature modification methods to preserve more aligned features. However, an important yet unexplored factor ultimately hinders their capability, which is the inherent differences in the temporal and spatial scale of information between visual and language inputs. To address this issue, we propose a novel visual-language tracker that enhances the effect of feature modification by \textbf{A}ligning \textbf{T}emporal and \textbf{S}patial scale of different input components, named as \textbf{ATSTrack}. Specifically, we decompose each language description into phrases with different attributes based on their temporal and spatial correspondence with visual inputs, and modify their features in a fine-grained manner. Moreover, we introduce a Visual-Language token that comprises modified linguistic information from the previous frame to guide the model to extract visual features that are more relevant to language description, thereby reducing the impact caused by the differences in spatial scale. Experimental results show that our proposed ATSTrack achieves performance comparable to existing methods. Our code will be released.