Localization-Based Tracking

TL;DR

This paper introduces Localization-based Tracking (LBT), a method that enhances real-time object tracking by focusing detection efforts on likely object regions, significantly improving speed and accuracy over existing methods.

Contribution

LBT extends existing trackers with a region-focused approach, achieving state-of-the-art performance and higher frame-rates on standard benchmarks.

Findings

LBT-extended trackers outperform previous algorithms on UA-DETRAC.

LBT-extended KIOU achieves 25% higher frame-rate and 1.1% better accuracy on UA-DETRAC.

LBT-extended SORT achieves 62% speedup and 3.2% accuracy increase on UA-DETRAC.

Abstract

End-to-end production of object tracklets from high resolution video in real-time and with high accuracy remains a challenging problem due to the cost of object detection on each frame. In this work we present Localization-based Tracking (LBT), an extension to any tracker that follows the tracking by detection or joint detection and tracking paradigms. Localization-based Tracking focuses only on regions likely to contain objects to boost detection speed and avoid matching errors. We evaluate LBT as an extension to two example trackers (KIOU and SORT) on the UA-DETRAC and MOT20 datasets. LBT-extended trackers outperform all other reported algorithms in terms of PR-MOTA, PR-MOTP, and mostly tracked objects on the UA-DETRAC benchmark, establishing a new state-of-the art. relative to tracking by detection with KIOU, LBT-extended KIOU achieves a 25% higher frame-rate and is 1.1% more accurate in terms of PR-MOTA on the UA-DETRAC dataset. LBT-extended SORT achieves a 62% speedup and a 3.2% increase in PR-MOTA on the UA-DETRAC dataset. On MOT20, LBT-extended KIOU has a 50% higher frame-rate than tracking by detection and is 0.4% more accurate in terms of MOTA. As of submission time, our LBT-extended KIOU tracker places 10th overall on the MOT20 benchmark.