This repository provides an implementation of (CVPR20) A Unified Object Motion and Affinity Model for Online Multi-Object Tracking (UMA-MOT). The work integrates single object tracking and metric learning into a unified triplet network by means of multi-task learning. Please refer the paper for the full details.
- python3
- tensorflow-gpu==1.15.0
- Clone this repo and install dependencies
pip3 install -r requirements.txt
-
Modify
config/config.pyto add the data path. -
Run the inference code on MOT16 or MOT17 benchmarks.
cd UMA-MOT/UMA-TEST
python3 test.py
- Refer py-motmetrics for evaluating the tracking results in
UMA-TEST/outputs.
cd UMA-MOT/motmetrics
python3 -m motmetrics.apps.eval_motchallenge DataPath/MOT-Challenge/MOT16/train ~/UMA-MOT/UMA-TEST/outputs/MOT16/MOT16_train-occ_0.8-ass_0.7-npair0.1-id0.1-se_block2-20200729_220301
- Visualization.
cd UMA-MOT/application_util
python3 show_results.py \
--sequence_dir=/home/junbo/datasets/MOT-Challenge/MOT16/train/MOT16-09 \
--result_file=output_path/MOT16-09.txt \
--detection_file=UMA-MOT/UMA-TEST/filtered_detections/MOT16-train
IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP IDt IDa IDm
MOT16-04 63.8% 84.4% 51.3% 55.2% 90.8% 83 18 39 26 2653 21315 43 330 49.5% 0.214 20 14 4
MOT16-11 66.2% 83.3% 55.0% 61.8% 93.6% 69 17 26 26 385 3504 35 71 57.2% 0.226 16 18 8
MOT16-05 57.0% 81.0% 43.9% 48.2% 89.0% 125 21 63 41 407 3529 37 135 41.7% 0.257 30 19 20
MOT16-13 48.0% 83.1% 33.7% 36.8% 90.7% 107 19 38 50 431 7235 27 176 32.8% 0.283 27 17 20
MOT16-10 65.6% 88.0% 52.2% 53.9% 90.8% 54 12 28 14 673 5682 35 227 48.1% 0.262 17 13 7
MOT16-09 70.5% 84.5% 60.6% 67.5% 94.2% 25 11 13 1 219 1706 27 56 62.9% 0.265 15 10 4
MOT16-02 44.3% 81.3% 30.4% 33.4% 89.0% 54 7 22 25 734 11884 24 169 29.1% 0.255 18 9 7
OVERALL 59.9% 84.1% 46.5% 50.3% 91.0% 517 105 229 183 5502 54855 228 1164 45.1% 0.236 143 100 70
If you find this project helpful in your research, please consider citing the following paper:
@inproceedings{yin2020unified,
title={A Unified Object Motion and Affinity Model for Online Multi-Object Tracking},
author={Yin, Junbo and Wang, Wenguan and Meng, Qinghao and Yang, Ruigang and Shen, Jianbing},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2020},
}