  Multi-target multi-camera tracking (MTMCT) plays an important role in
intelligent video analysis, surveillance video retrieval, and other application
scenarios. Nowadays, the deep-learning-based MTMCT has been the mainstream and
has achieved fascinating improvements regarding tracking accuracy and
efficiency. However, according to our investigation, the lacking of datasets
focusing on real-world application scenarios limits the further improvements
for current learning-based MTMCT models. Specifically, the learning-based MTMCT
models training by common datasets usually cannot achieve satisfactory results
in real-world application scenarios. Motivated by this, this paper presents a
semi-automatic data annotation system to facilitate the real-world MTMCT
dataset establishment. The proposed system first employs a deep-learning-based
single-camera trajectory generation method to automatically extract
trajectories from surveillance videos. Subsequently, the system provides a
recommendation list in the following manual cross-camera trajectory matching
process. The recommendation list is generated based on side information,
including camera location, timestamp relation, and background scene. In the
experimental stage, extensive results further demonstrate the efficiency of the
proposed system.
