Datasets:
metadata
license: apache-2.0
task_categories:
- object-detection
tags:
- multi-object-tracking
- mot
- pedestrian
- cctv
- person-tracking
pretty_name: CrowdTrack-MOT
CrowdTrack-MOT
Pedestrian multi-object-tracking benchmark in MOTChallenge format, derived from
Loseevaya/CrowdTrack.
33 CCTV / surveillance sequences, single class person, ~702k GT boxes, 1920×1080.
What changed vs. the original
- Converted to MOT format
and added a
seqinfo.iniper sequence. The original ships two heterogeneous annotation formats (a few sequences as plaingt.txt, the rest as per-frame LabelMe JSON) — both are unified into a single MOTgt.txt. (frameRate=25inseqinfo.iniis a hardcoded placeholder — the original FPS is unknown.) - Boxes clipped to the frame. Some original boxes extended past the image border (and a few were stored with inverted/negative width-height); these are normalized to top-left + positive size and clipped to the frame bounds.
Layout
train/
trackNNNN/
img1/000001.<ext> ... # frames, 1-indexed MOT naming (.jpg or .png)
gt/gt.txt # frame,id,x,y,w,h,1,1,1 (class: person)
seqinfo.ini
Download
hf download Fleyderer/CrowdTrack-MOT --repo-type dataset --local-dir CrowdTrack-MOT
Works as a benchmark in BoxMOT:
boxmot eval --benchmark crowdtrack-mot --detector <det> --reid <reid> --tracker <trk>
Credits
- Original benchmark: CrowdTrack: A Benchmark for Difficult Multiple Pedestrian Tracking in Real Scenarios — Teng Fu, Yuwen Chen, Zhuofan Chen, Mengyang Zhao, Bin Li, Xiangyang Xue (arXiv:2507.02479).
- Source data: Loseevaya/CrowdTrack — all imagery and original annotations.
- MOT conversion / clipping / packaging: Fleyderer.
Citation
If you use this dataset, please cite the original CrowdTrack paper:
@article{fu2025crowdtrack,
title = {CrowdTrack: A Benchmark for Difficult Multiple Pedestrian Tracking in Real Scenarios},
author = {Fu, Teng and Chen, Yuwen and Chen, Zhuofan and Zhao, Mengyang and Li, Bin and Xue, Xiangyang},
journal = {arXiv preprint arXiv:2507.02479},
year = {2025}
}
