LITE: A Paradigm Shift in Multi-Object Tracking with Efficient ReID Feature Integration
<|reference_start|>LITE: A Paradigm Shift in Multi-Object Tracking with Efficient ReID Feature Integration: The Lightweight Integrated Tracking-Feature Extraction (LITE) paradigm is introduced as a novel multi-object tracking (MOT) approach. It enhances ReID-based trackers by eliminating inference, pre-processing, post-processing, and ReID model training costs. LITE uses real-time appearance features without compromising speed. By integrating appearance feature extraction directly into the tracking pipeline using standard CNN-based detectors such as YOLOv8m, LITE demonstrates significant performance improvements. The simplest implementation of LITE on top of classic DeepSORT achieves a HOTA score of 43.03% at 28.3 FPS on the MOT17 benchmark, making it twice as fast as DeepSORT on MOT17 and four times faster on the more crowded MOT20 dataset, while maintaining similar accuracy. Additionally, a new evaluation framework for tracking-by-detection approaches reveals that conventional trackers like DeepSORT remain competitive with modern state-of-the-art trackers when evaluated under fair conditions. The code will be available post-publication at https://github.com/Jumabek/LITE.<|reference_end|>
@article{alikhanov2024lite:,
    title={LITE: A Paradigm Shift in Multi-Object Tracking with Efficient ReID
  Feature Integration},
    author={Jumabek Alikhanov, Dilshod Obidov, Hakil Kim},
    journal={arXiv preprint arXiv:2409.04187},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04187},
    primaryClass={cs.CV}
}