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.

翻译：本文提出了一种新颖的多目标跟踪（MOT）方法——轻量级集成跟踪-特征提取（LITE）范式。它通过消除推理、预处理、后处理以及ReID模型训练的成本，增强了基于ReID的跟踪器。LITE在不牺牲速度的前提下，利用实时外观特征。通过使用诸如YOLOv8m等标准基于CNN的检测器，将外观特征提取直接集成到跟踪流程中，LITE展示了显著的性能提升。在经典DeepSORT基础上最简单的LITE实现在MOT17基准测试上实现了43.03%的HOTA分数，帧率达到28.3 FPS，使其在MOT17上的速度是DeepSORT的两倍，在更拥挤的MOT20数据集上的速度是DeepSORT的四倍，同时保持了相似的精度。此外，一种新的基于检测的跟踪方法评估框架揭示，在公平条件下评估时，像DeepSORT这样的传统跟踪器与现代最先进的跟踪器相比仍然具有竞争力。代码将在发表后发布于 https://github.com/Jumabek/LITE。