Abundant resources related to Multi-Target Multi-Camera Object Tracking (MTMCT) 🔥
In the visual tracking community, the dominant research focus used to be around tracking either a single object (SOT) or multiple objects (MOT) within a single camera. However, with the increasing adoption of multi-camera networks across various applications, the demand for multi-camera tracking systems has surged, surpassing the usage of single cameras [1].
We provide a comprehensive and up-to-date review of visual object tracking in multi-camera settings. The study analyzes and categorize existing works based on six crucial facets: problem formulation, adopted problem solving approach, data association requirements, mutual exclusion constraints, benchmark datasets, and performance metrics.
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[2023-07-11] 🔥 We have released this repository that collects the resources related to Multi-Target Multi-Camera Object Tracking (MTMCT). We will keep updating this repository, and you are welcome to STAR and WATCH to keep track of it.
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[2023-07-08] 🔥 Our review paper with the title "Multi-Camera Multi-Object Tracking: A Review of Current Trends and Future Advances" (ver. 06 Jul) was accepted for publication in Neurocomputing
- Diffusion convolutional recurrent neural network: Data-driven traffic forecasting (ICLR, 2018) [paper]
- Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting (IJCAI, 2018) [paper]
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