Multiple Object Tracking Challenge Technical Report for Team MT_IoT
Not on the map. Why?
Abstract (arXiv)
This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage task including human detection and trajectory matching. Specifically, we designed an improved human detector and associated most of detection to guarantee the integrity of the motion trajectory. We also propose a location-wise matching matrix to obtain more accurate trace matching. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 7 Dec 2022.
Builds on
8 of 10 references.
References · 8
- 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,368
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
- 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 823
- 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
- 2020MOT20: A benchmark for multi object tracking in crowded scenescited by 357
- 2022Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Trackingcited by 346
- 2022BoT-SORT: Robust Associations Multi-Pedestrian Trackingcited by 320
- 2018CrowdHuman: A Benchmark for Detecting Human in a Crowdcited by 284
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Citing papers per year: 2023: 2; 2024: 1.
3 papers.