FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking
<|reference_start|>FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking: Multiple object tracking (MOT) involves identifying multiple targets and assigning them corresponding IDs within a video sequence, where occlusions are often encountered. Recent methods address occlusions using appearance cues through online learning techniques to improve adaptivity or offline learning techniques to utilize temporal information from videos. However, most existing online learning-based MOT methods are unable to learn from all past tracking information to improve adaptivity on long-term occlusions while maintaining real-time tracking speed. On the other hand, temporal information-based offline learning methods maintain a long-term memory to store past tracking information, but this approach restricts them to use only local past information during tracking. To address these challenges, we propose a new MOT framework called the Feature Adaptive Continual-learning Tracker (FACT), which enables real-time tracking and feature learning for targets by utilizing all past tracking information. We demonstrate that the framework can be integrated with various state-of-the-art feature-based trackers, thereby improving their tracking ability. Specifically, we develop the feature adaptive continual-learning (FAC) module, a neural network that can be trained online to learn features adaptively using all past tracking information during tracking. Moreover, we also introduce a two-stage association module specifically designed for the proposed continual learning-based tracking. Extensive experiment results demonstrate that the proposed method achieves state-of-the-art online tracking performance on MOT17 and MOT20 benchmarks. The code will be released upon acceptance.<|reference_end|>
@article{song2024fact:,
    title={FACT: Feature Adaptive Continual-learning Tracker for Multiple Object
  Tracking},
    author={Rongzihan Song, Zhenyu Weng, Huiping Zhuang, Jinchang Ren, Yongming
  Chen, and Zhiping Lin},
    journal={arXiv preprint arXiv:2409.07904},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07904},
    primaryClass={cs.CV}
}