@inproceedings{long2018tracking,
title={Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identification},
author={Long, Chen and Haizhou, Ai and Zijie, Zhuang and Chong, Shang},
year={2018},
booktitle={ICME}
}
Download MOT16 dataset and trained weights from the following links.
Put weight files in data, then build and run the code.
pip install -r requirements.txt
sh make.sh
python eval_mot.pyI used five of six training sequences as the validation set. Following are the details and evaluation results. Please note that the results may be a little different with the paper because this is a re-implementation version.
Sequences:
'MOT16-02'
'MOT16-05'
'MOT16-09'
'MOT16-11'
'MOT16-13'
... MOT16-02
Preprocessing (cleaning) MOT16-02...
......
Removing 656 boxes from solution...
MOT16-02
IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
38.0 76.4 25.3| 30.6 92.5 0.73| 54 7 20 27| 441 12379 47 146| 27.8 75.1 28.1
... MOT16-05
Preprocessing (cleaning) MOT16-05...
........
Removing 1 boxes from solution...
MOT16-05
IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
52.0 80.8 38.3| 44.3 93.3 0.26| 125 12 68 45| 216 3801 35 130| 40.6 76.1 41.1
... MOT16-09
Preprocessing (cleaning) MOT16-09...
.....
Removing 765 boxes from solution...
MOT16-09
IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
58.6 73.1 48.9| 63.2 94.5 0.37| 25 7 16 2| 195 1936 35 66| 58.8 75.2 59.4
... MOT16-11
Preprocessing (cleaning) MOT16-11...
.........
Removing 2 boxes from solution...
MOT16-11
IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
54.3 71.6 43.7| 57.7 94.5 0.34| 69 11 29 29| 309 3884 29 74| 54.0 79.3 54.3
... MOT16-13
Preprocessing (cleaning) MOT16-13...
.......
Removing 0 boxes from solution...
MOT16-13
IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
38.0 71.7 25.9| 29.5 81.8 1.01| 107 11 39 57| 754 8072 46 178| 22.5 72.6 22.9
********************* Your MOT16 Results *********************
IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
45.7 74.4 33.0| 40.5 91.4 0.53| 380 48 172 160| 1915 30072 192 594| 36.3 75.9 36.7
You can use official matlab eval devkit to evaluate the outputs.
Or directly use the python version motmetrics.
I already added the python evaluation method in the eval_mot.py script.
The results are slightly different from the official devkit since the ignoring method is not identical.
Results from python evaluation:
IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP
MOT16-02 37.1% 75.6% 24.6% 30.2% 93.0% 54 7 21 26 406 12440 47 146 27.7% 0.247
MOT16-05 53.7% 83.0% 39.7% 44.6% 93.1% 125 13 68 44 224 3779 35 130 40.8% 0.242
MOT16-09 61.1% 75.8% 51.1% 63.6% 94.3% 25 8 15 2 202 1913 28 64 59.2% 0.247
MOT16-11 54.9% 72.2% 44.3% 58.1% 94.7% 69 12 28 29 301 3840 27 70 54.6% 0.208
MOT16-13 38.2% 71.6% 26.0% 29.7% 81.6% 107 11 38 58 766 8051 46 178 22.6% 0.276
OVERALL 46.1% 75.1% 33.3% 40.6% 91.5% 380 51 170 159 1899 30023 183 588 36.5% 0.241
Paper: Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identification (researchgate, arxiv)
Results on the test set: https://motchallenge.net/tracker/MOTDT
Eval Devkit: https://bitbucket.org/amilan/motchallenge-devkit/
Models: https://drive.google.com/open?id=1ETfqSoy7OeT-8GO75F1bYWhP3mzrwwvn