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Welcome to the Yolov5_DeepSort_Pytorch wiki!
- Evaluation
- Download MOT16 data from:
https://motchallenge.net/data/MOT16/
under downloads. The structure of the downloaded data:
MOT16
├── train
│ ├── MOT16-02
│ │ ├── det
│ │ ├── gt
│ │ ├── img1
│ │ └── seqinfo.ini
│ ├── MOT16-04
│ ├── MOT16-05
│ ├── MOT16-09
│ ├── MOT16-10
│ ├── MOT16-11
│ └── MOT16-13
│
└── test
├── MOT16-02
├── MOT16-04
├── MOT16-05
├── MOT16-09
├── MOT16-10
├── MOT16-11
└── MOT16-13
- Transform all images under all img1 under each MOT folder into videos by:
ffmpeg -framerate 25 -i %06d.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p MOT16-13.mp4
- Then run:
python3 track.py --source /home/mikel.brostrom/Documents/TrackEval/data/MOT16/train/MOT16-XX/MOT16-XX.mp4 –save-txt
on all the videos and save the results under this cloned repo:
https://github.com/JonathonLuiten/TrackEval (which according to: https://github.com/dendorferpatrick/MOTChallengeEvalKit is 'the new official python (MOT) evaluation code)
by creating a folder to store the results for the different sequences generated by the yolov5_deep_sort algorithm like this:
trackeval
├── data
├── trackers
├── mot_challange
├── MOT16-train
├── ch_yolov5m_deep_sort
├── MOT16-02.txt
├── MOT16-04.txt
├── MOT16-05.txt
├── MOT16-09.txt
├── MOT16-10.txt
├── MOT16-11.txt
├── MOT16-13.txt
Then you can run the combined evaluation (on all sequences) by:
cd trackeval
python scripts/run_mot_challenge.py --BENCHMARK MOT16 --TRACKERS_TO_EVAL ch_yolov5m_deep_sort --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 4
if you want to evaluate a single sequence you can use the --SEQ_INFO flag:
python scripts/run_mot_challenge.py --SEQ_INFO MOT16-04 --BENCHMARK MOT16 --TRACKERS_TO_EVAL humancrowd_yolov5_deep_sort --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1
NOTICE! That this evaluation is on the train MOT dataset
Obtained metrics:
CLEAR: humancrowd_yolov5_deep_sort-pedestrianMOTA MOTP MODA CLR_Re CLR_Pr MTR PTR MLR sMOTA CLR_TP CLR_FN CLR_FP IDSW MT PT ML Frag
MOT16-02 34.363 76.061 34.969 42.186 85.392 16.667 42.593 40.741 24.265 7523 10310 1287 108 9 23 22 240
MOT16-04 60.09 75.95 60.298 74.176 84.239 42.169 40.964 16.867 42.25 35276 12281 6600 99 35 34 14 341
MOT16-05 54.488 76.493 55.603 72.998 80.756 44 47.2 8.8 37.328 4977 1841 1186 76 55 59 11 156
MOT16-09 60.167 81.933 61.138 78.258 82.05 56 36 8 46.029 4114 1143 900 51 14 9 2 74
MOT16-10 52.931 74.668 53.824 64.166 86.119 29.63 53.704 16.667 36.676 7904 4414 1274 110 16 29 9 333
MOT16-11 58.208 83.949 58.633 80.663 78.548 55.072 34.783 10.145 45.261 7400 1774 2021 39 38 24 7 82
MOT16-13 30.934 68.282 31.948 47.066 75.688 12.15 57.944 29.907 16.006 5389 6061 1731 116 13 62 32 330
COMBINED 51.614 76.444 52.156 65.741 82.874 34.816 46.422 18.762 36.128 72583 37824 14999 599 180 240 97 1556
Identity: humancrowd_yolov5_deep_sort-pedestrianIDF1 IDR IDP IDTP IDFN IDFP
MOT16-02 32.556 24.32 49.228 4337 13496 4473
MOT16-04 64.234 60.397 68.591 28723 18834 13153
MOT16-05 41.353 39.366 43.55 2684 4134 3479
MOT16-09 51.757 50.561 53.012 2658 2599 2356
MOT16-10 56.792 49.553 66.507 6104 6214 3074
MOT16-11 50.605 51.286 49.942 4705 4469 4716
MOT16-13 36.931 29.948 48.16 3429 8021 3691
COMBINED 53.175 47.678 60.104 52640 57767 34942
Count: humancrowd_yolov5_deep_sort-pedestrianDets GT_Dets IDs GT_IDs
MOT16-02 8810 17833 57 54
MOT16-04 41876 47557 115 83
MOT16-05 6163 6818 43 125
MOT16-09 5014 5257 25 25
MOT16-10 9178 12318 52 54
MOT16-11 9421 9174 62 69
MOT16-13 7120 11450 59 107
COMBINED 87582 110407 413 517