Localization-Based Tracking
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 57 references that are in the map. First posted on arXiv 12 Apr 2021.
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32 of 57 references.
References · 32
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2017Mask R-CNNcited by 7,259
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2015SSD: Single Shot MultiBox Detectorcited by 3,125
- 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 908
- 2016Simple Online and Realtime Trackingcited by 862
- 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
- 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
- 2016MOT16: A Benchmark for Multi-Object Trackingcited by 531
- 2020FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Trackingcited by 489
- 2020Tracking Objects as Pointscited by 483
- 2019Tracking without bells and whistlescited by 382
20 more
- 2020MOT20: A benchmark for multi object tracking in crowded scenescited by 355
- 2019Towards Real-Time Multi-Object Trackingcited by 311
- 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
- 2014Multiple Object Tracking: A Literature Reviewcited by 165
- 2017Detect to Track and Track to Detectcited by 131
- 2020Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Trackingcited by 115
- 2020Rethinking the competition between detection and ReID in Multi-Object Trackingcited by 101
- 2015UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Trackingcited by 98
- 2016Learning to Track at 100 FPS with Deep Regression Networkscited by 89
- 2016Deep Watershed Transform for Instance Segmentationcited by 77
- 2020TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Modelcited by 70
- 2017Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanismcited by 48
- 2020Simple Unsupervised Multi-Object Trackingcited by 33
- 2020Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Trackingcited by 25
- 2019CVPR19 Tracking and Detection Challenge: How crowded can it get?cited by 22
- 2020SpotNet: Self-Attention Multi-Task Network for Object Detectioncited by 14
- 2020Joint Object Detection and Multi-Object Tracking with Graph Neural Networkscited by 14
- 2017Evolving Boxes for Fast Vehicle Detectioncited by 6
- 2017SOT for MOTcited by 3
- 2020SMOT: Single-Shot Multi Object Trackingcited by 2
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