Forked from https://github.com/GBJim/iou_tracker Modified by rudcode
Python implementation of the IOU Tracker described in the AVSS 2017 paper High-Speed Tracking-by-Detection Without Using Image Information.
This project is released under the MIT License (details in LICENSE file). If you think our work is useful in your research, please consider citing:
@INPROCEEDINGS{1517Bochinski2017,
AUTHOR = {Erik Bochinski and Volker Eiselein and Thomas Sikora},
TITLE = {High-Speed Tracking-by-Detection Without Using Image Information},
BOOKTITLE = {International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017},
YEAR = {2017},
MONTH = aug,
ADDRESS = {Lecce, Italy},
URL = {http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf},
}
Several demo scripts are included to reproduce the reported results on the UA-DETRAC and the MOT 16/17 benchmarks.
Basic demo script:
$ ./demo.py -h
usage: demo.py [-h] -d DETECTION_PATH -o OUTPUT_PATH [-sl SIGMA_L]
[-sh SIGMA_H] [-si SIGMA_IOU] [-tm T_MIN]
IOU Tracker demo script
optional arguments:
-h, --help show this help message and exit
-d DETECTION_PATH, --detection_path DETECTION_PATH
full path to CSV file containing the detections
-o OUTPUT_PATH, --output_path OUTPUT_PATH
output path to store the tracking results (MOT
challenge devkit compatible format)
-sl SIGMA_L, --sigma_l SIGMA_L
low detection threshold
-sh SIGMA_H, --sigma_h SIGMA_H
high detection threshold
-si SIGMA_IOU, --sigma_iou SIGMA_IOU
intersection-over-union threshold
-tm T_MIN, --t_min T_MIN
minimum track length
Example for the MOT17-04 sequence (detections can be downloaded here):
./demo.py -d ../mot17/train/MOT17-04-SDP/det/det.txt -o res/iou-tracker/MOT17-04-SDP.txt
To reproduce the reported results, download and extract the DETRAC-toolkit and the detections you want to evaluate. Download links for the EB detections are provided below. Clone this repository into "DETRAC-MOT-toolkit/trackers/". Follow the instructions to configure the toolkit for tracking evaluation and set the tracker name in "DETRAC_experiment.m":
tracker.trackerName = 'iou-tracker';
and run the script.
Note that you still need a working python environment with numpy installed. You should obtain something like the following results for the 'DETRAC-Train' set:
| Detector | PR-Rcll | PR-Prcn | PR-FAR | PR-MT | PR-PT | PR-ML | PR-FP | PR-FN | PR-IDs | PR-FM | PR-MOTA | PR-MOTP | PR-MOTAL |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EB | 37.86 | 44.73 | 0.10 | 32.34 | 12.88 | 20.93 | 7958.82 | 163739.85 | 4129.40 | 4221.89 | 35.77 | 40.81 | 36.48 |
| R-CNN | 27.86 | 52.90 | 0.11 | 19.53 | 17.03 | 18.56 | 9047.95 | 157521.18 | 4842.18 | 4969.57 | 25.46 | 44.39 | 26.29 |
| CompACT | 25.15 | 49.64 | 0.09 | 18.40 | 14.15 | 18.91 | 7681.50 | 152078.88 | 2177.44 | 2282.27 | 23.44 | 42.88 | 23.8191 |
| ACF | 27.39 | 52.68 | 0.14 | 20.24 | 15.66 | 19.40 | 11553.49 | 161293.27 | 1845.49 | 2101.44 | 25.07 | 44.71 | 25.39 |
The reference results are taken from the UA-DETRAC results site. Only the best tracker / detector combination is displayed for each reference method.
| Tracker | Detector | PR-MOTA | PR-MOTP | PR-MT | PR-ML | PR-IDs | PR-FM | PR-FP | PR-FN | Speed |
|---|---|---|---|---|---|---|---|---|---|---|
| CEM | CompACT | 5.1% | 35.2% | 3.0% | 35.3% | 267.9 | 352.3 | 12341.2 | 260390.4 | 4.62 fps |
| CMOT | CompACT | 12.6% | 36.1% | 16.1% | 18.6% | 285.3 | 1516.8 | 57885.9 | 167110.8 | & 3.79 fps |
| GOG | CompACT | 14.2% | 37.0% | 13.9% | 19.9% | 3334.6 | 3172.4 | 32092.9 | 180183.8 | 390 fps |
| DCT | R-CNN | 11.7% | 38.0% | 10.1% | 22.8% | 758.7 | 742.9 | 336561.2 | 210855.6 | 0.71 fps |
| H2T | CompACT | 12.4% | 35.7% | 14.8% | 19.4% | 852.2 | 1117.2 | 51765.7 | 173899.8 | 3.02 fps |
| IHTLS | CompACT | 11.1% | 36.8% | 13.8% | 19.9% | 953.6 | 3556.9 | 53922.3 | 180422.3 | 19.79 fps |
| IOU | R-CNN | 16.0% | 38.3% | 13.8% | 20.7% | 5029.4 | 5795.7 | 22535.1 | 193041.9 | 100,840 fps |
| IOU | EB | 19.4% | 28.9% | 17.7% | 18.4% | 2311.3 | 2445.9 | 14796.5 | 171806.8 | 6,902 fps |
The public detections of EB are not available on the DETRAC training set and miss some low scoring detections. The EB detections we used for the tables above and our publication are available here:
The IOU Tracker was evaluated on the MOT17 benchmark as well. To determine the best parameters for each detector, an exhaustive search of the parameter space was performed similar to the one of the MOT16 evaluation reported in the paper. The best configuration for the training sequences is:
| Detector | ||||
|---|---|---|---|---|
| DPM | -0.5 | 0.5 | 0.4 | 4 |
| FRCNN | 0.0 | 0.9 | 0.3 | 3 |
| SPD | 0.4 | 0.5 | 0.2 | 2 |
To generate the MOT17 results listed at MOT17 results, use the mot17.py script. Note that the parameters from above are hard-coded in the script for your convenience.
usage: mot17.py [-h] -m SEQMAP -o RES_DIR -b BENCHMARK_DIR
IOU Tracker MOT17 demo script. The best parameters for each detector are
hardcoded.
optional arguments:
-h, --help show this help message and exit
-m SEQMAP, --seqmap SEQMAP
full path to the seqmap file to evaluate
-o RES_DIR, --res_dir RES_DIR
path to the results directory
-b BENCHMARK_DIR, --benchmark_dir BENCHMARK_DIR
path to the sequence directory
Examples (you will probably need to adapt the paths):
./mot17.py -m ../motchallenge/seqmaps/c10-train.txt -o ../motchallenge/res/MOT17/iou-tracker -b ../data/mot17/train
./mot17.py -m ../motchallenge/seqmaps/c10-test.txt -o ../motchallenge/res/MOT17/iou-tracker -b ../data/mot17/test
| Detector | IDF1 | IDP | IDR | Rcll | Prcn | FAR | GT | MT | PT | ML | FP | FN | IDs | FM | MOTA | MOTP | MOTAL |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DPM | 14.3 | 39.3 | 8.7 | 35.8 | 88.1 | 1.02 | 546 | 45 | 195 | 306 | 5420 | 72140 | 719 | 844 | 30.3 | 77.1 | 30.9 |
| FRCNN | 21.6 | 47.5 | 14.0 | 52.1 | 97.0 | 0.34 | 546 | 111 | 268 | 167 | 1804 | 53774 | 857 | 876 | 49.7 | 88.1 | 50.5 |
| SDP | 24.4 | 44.5 | 16.8 | 66.8 | 96.8 | 0.47 | 546 | 197 | 240 | 109 | 2509 | 37280 | 2058 | 2065 | 62.7 | 83.2 | 64.6 |
| All | 9.9 | 21.5 | 6.4 | 51.6 | 94.7 | 0.61 | 1638 | 353 | 703 | 582 | 97331 | 63194 | 3634 | 3785 | 47.6 | 83.4 | 48.7 |
| MOTA | MOTP | FAF | MT | ML | FP | FN | ID Sw. | Frag |
|---|---|---|---|---|---|---|---|---|
| 45.5 | 76.9 | 1.1 | 15.7% | 40.5% | 19,993 | 281,643 | 5,988 | 7,404 |
To reproduce the reported MOT16 results of the paper, use the mot16.py script:
$ ./mot16.py -h
usage: mot16.py [-h] -m SEQMAP -o RES_DIR -b BENCHMARK_DIR [-sl SIGMA_L]
[-sh SIGMA_H] [-si SIGMA_IOU] [-tm T_MIN]
IOU Tracker MOT demo script. Default parameters are set to reproduce the
results using the SDP detections.
optional arguments:
-h, --help show this help message and exit
-m SEQMAP, --seqmap SEQMAP
full path to the seqmap file to evaluate
-o RES_DIR, --res_dir RES_DIR
path to the results directory
-b BENCHMARK_DIR, --benchmark_dir BENCHMARK_DIR
path to the sequence directory
-sl SIGMA_L, --sigma_l SIGMA_L
low detection threshold
-sh SIGMA_H, --sigma_h SIGMA_H
high detection threshold
-si SIGMA_IOU, --sigma_iou SIGMA_IOU
intersection-over-union threshold
-tm T_MIN, --t_min T_MIN
minimum track length
Examples (you will probably need to adapt the paths):
# SDP:
./mot16.py -m ../motchallenge/seqmaps/sdp-train.txt -o ../motchallenge/res/MOT16/iou-tracker -b ../data/mot17/train
# FRCNN:
./mot16.py -m ../motchallenge/seqmaps/frcnn-train.txt -o ../motchallenge/res/MOT16/iou-tracker -b ../data/mot17/train -sl 0 -sh 0.9 -si 0.3 -tm 5
The seqmap files can be found under "seqmaps" and need to be copied to the respective directory of the motchallenge devkit. You should obtain something like the following results for the train set:
| Detector | IDF1 | IDP | IDR | Rcll | Prcn | FAR | GT | MT | PT | ML | FP | FN | IDs | FM | MOTA | MOTP | MOTAL |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SDP | 24.7 | 46.2 | 16.9 | 65.0 | 97.6 | 0.34 | 546 | 178 | 232 | 136 | 1796 | 39348 | 1198 | 1453 | 62.3 | 83.4 | 63.4 |
| FRCNN | 21.0 | 46.5 | 13.6 | 51.8 | 97.2 | 0.31 | 546 | 109 | 261 | 176 | 1674 | 54082 | 716 | 810 | 49.7 | 88.2 | 50.3 |
| Detector | Rcll | Prcn | FAR | GT | MT | PT | ML | FP | FN | IDs | FM | MOTA | MOTP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SDP | 61.5 | 95.2 | 0.96 | 759 | 179 | 330 | 250 | 5702 | 70278 | 2167 | 3028 | 57.1 | 77.1 |
| FRCNN | 50.9 | 92.4 | 1.29 | 759 | 113 | 381 | 265 | 7639 | 89535 | 2284 | 2310 | 45.4 | 77.5 |
Please note that this evaluation already includes the new ground truth of the MOT17 release.
If you have any questions or encounter problems regarding the method/code feel free to contact me at [email protected]