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.

;Comment: 15 pages, 6 figures, to be published in ICONIP-2024