Localization-Based Tracking
Derek Gloudemans, Daniel B. Work

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…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Neural Network Applications · Robotics and Sensor-Based Localization
