  In the task of multi-target multi-camera (MTMC) tracking of pedestrians, the
data association problem is a key issue and main challenge, especially with
complications arising from camera movements, lighting variations, and
obstructions. However, most MTMC models adopt two-step approaches, thus heavily
depending on the results of the first-step tracking in practical applications.
Moreover, the same targets crossing different cameras may exhibit significant
appearance variations, which further increases the difficulty of cross-camera
matching. To address the aforementioned issues, we propose a global online MTMC
tracking model that addresses the dependency on the first tracking stage in
two-step methods and enhances cross-camera matching. Specifically, we propose a
transformer-based global MTMC association module to explore target associations
across different cameras and frames, generating global trajectories directly.
Additionally, to integrate the appearance and spatio-temporal features of
targets, we propose a feature extraction and fusion module for MTMC tracking.
This module enhances feature representation and establishes correlations
between the features of targets across multiple cameras. To accommodate high
scene diversity and complex lighting condition variations, we have established
the VisionTrack dataset, which enables the development of models that are more
generalized and robust to various environments. Our model demonstrates
significant improvements over comparison methods on the VisionTrack dataset and
others.
