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‎README.md‎

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# IOU-Tracker
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## IOU Tracker
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Python implementation of the IOU Tracker described in the AVSS 2017 paper
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[High-Speed Tracking-by-Detection Without Using Image Information](http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf).
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coming soon...
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This project is released under the MIT License (details in LICENSE file).
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If you think our work is useful in your research, please consider citing:
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```
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@INPROCEEDINGS{1517Bochinski2017,
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AUTHOR = {Erik Bochinski and Volker Eiselein and Thomas Sikora},
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TITLE = {High-Speed Tracking-by-Detection Without Using Image Information},
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BOOKTITLE = {International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017},
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YEAR = {2017},
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MONTH = aug,
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ADDRESS = {Lecce, Italy},
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URL = {http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf},
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}
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```
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## Demo
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Several demo scripts are included to reproduce the reported results on the UA-DETRAC
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and the MOT benchmark.
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Basic demo script:
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```
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$ ./demo.py -h
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usage: demo.py [-h] -d DETECTION_PATH -o OUTPUT_PATH [-sl SIGMA_L]
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[-sh SIGMA_H] [-si SIGMA_IOU] [-tm T_MIN]
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IOU Tracker demo script
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optional arguments:
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-h, --help show this help message and exit
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-d DETECTION_PATH, --detection_path DETECTION_PATH
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full path to CSV file containing the detections
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-o OUTPUT_PATH, --output_path OUTPUT_PATH
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output path to store the tracking results (MOT
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challenge devkit compatible format)
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-sl SIGMA_L, --sigma_l SIGMA_L
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low detection threshold
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-sh SIGMA_H, --sigma_h SIGMA_H
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high detection threshold
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-si SIGMA_IOU, --sigma_iou SIGMA_IOU
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intersection-over-union threshold
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-tm T_MIN, --t_min T_MIN
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minimum track length
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```
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Example:
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```
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./demo.py -d ../mot17/train/MOT17-04-SDP/det/det.txt -o res/iou-tracker/MOT17-04-SDP.txt
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```
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### DETRAC
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To reproduce the reported results, download and extract the [DETRAC-toolkit](http://detrac-db.rit.albany.edu/download)
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and the detections you want to evaluate. Download links for the EB detections are provided below.
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Clone this repository into "DETRAC-MOT-toolkit/trackers/".
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Follow the instructions to configure the toolkit for tracking evaluation and set the tracker name in "DETRAC_experiment.m":
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```
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tracker.trackerName = 'iou-tracker';
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```
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and run the script.
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Note that you still need a working python environment.
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You should obtain something like the following results for the 'DETRAC-Train' set:
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##### DETRAC-Train Results
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| Detector | Rcll | Prcn | FAR | MT | PT | ML | FP | FN | IDs | FM | MOTA | MOTP | MOTAL |
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| -------- | ---- | ---- | --- | ----- | --- | ------ | ------- | ------- | ----- | ----- |----- |------|-------|
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| EB |22.12 |31.53 |0.26 |17.65 |13.22|18.41 |14796.52 |171806.84|2311.25|2445.89|19.41 |28.89 |19.78 |
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| 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 |
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##### DETRAC-Test (Overall) Results
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| Detector | Rcll | Prcn | FAR | MT | PT | ML | FP | FN | IDs | FM | MOTA | MOTP | MOTAL |
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| -------- | ---- | ---- | --- | ----- | --- | ------ | ------- | ------- | ----- | ----- |----- |------|-------|
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| EB |22.11 |31.53 |0.26 |17.65 |13.22|18.41 |14796.52 |171806.84|2311.25|2445.89|19.41 |28.89 |19.77 |
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| R-CNN |20.37 |44.86 |0.40 |13.81 |16.40|20.69 |22535.15 |193041.87|5029.42|5795.73|16.01 |38.35 |16.81 |
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##### EB detections
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The public detections of [EB](http://zyb.im/research/EB/) are not available on the
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DETRAC training set and miss some low scoring detections. The EB detections we used for the tables above and our
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publication are available here:
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* [EB Train](https://tubcloud.tu-berlin.de/s/EtC6cFEYsAU0gFQ/download)
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* [EB Test](https://tubcloud.tu-berlin.de/s/oKM3dYhJbMFl1dY/download)
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### MOT
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To reproduce the reported MOT16 results, use the mot16.py script:
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```
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$ ./mot16.py -h
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usage: mot16.py [-h] -m SEQMAP -o RES_DIR -b BENCHMARK_DIR [-sl SIGMA_L]
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[-sh SIGMA_H] [-si SIGMA_IOU] [-tm T_MIN]
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IOU Tracker MOT demo script. Default parameters are set to reproduce the
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results using the SDP detections.
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optional arguments:
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-h, --help show this help message and exit
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-m SEQMAP, --seqmap SEQMAP
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full path to the seqmap file to evaluate
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-o RES_DIR, --res_dir RES_DIR
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path to the results directory
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-b BENCHMARK_DIR, --benchmark_dir BENCHMARK_DIR
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path to the sequence directory
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-sl SIGMA_L, --sigma_l SIGMA_L
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low detection threshold
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-sh SIGMA_H, --sigma_h SIGMA_H
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high detection threshold
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-si SIGMA_IOU, --sigma_iou SIGMA_IOU
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intersection-over-union threshold
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-tm T_MIN, --t_min T_MIN
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minimum track length
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```
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Example:
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```
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# SDP:
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./mot16.py -m ../motchallenge/seqmaps/sdp-train.txt -o ../motchallenge/res/MOT16/iou-tracker -b ../data/mot17/train
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# FRCNN:
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./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
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```
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You should obtain something like the following results for the train set:
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##### MOT16 Train Results
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| Detector | IDF1 | IDP | IDR | Rcll | Prcn | FAR | GT | MT | PT | ML | FP | FN | IDs | FM | MOTA | MOTP | MOTAL |
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| -------- | ---- | --- | --- | ---- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---- | ---- | ----- |
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|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 |
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|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 |
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##### MOT16 Test Results
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| Detector | Rcll | Prcn | FAR | GT | MT | PT | ML | FP | FN | IDs | FM | MOTA | MOTP |
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| -------- | ---- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---- | ---- |
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|SDP |61.5 |95.2 |0.96 |759 |179 |330 |250 |5702 |70278|2167 |3028 |57.1 |77.1 |
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|FRCNN |50.9 |92.4 |1.29 |759 |113 |381 |265 |7639 |89535| 2284|2310 |45.4 |77.5 |
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Please note that this evaluation already includes the new ground truth of the MOT17 release.
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## Contact
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If you have any questions or encounter problems regarding the method/code feel free to contact me
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at [email protected]

‎demo.py‎

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#!/usr/bin/env python
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# ---------------------------------------------------------
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# IOU Tracker
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# Copyright (c) 2017 TU Berlin, Communication Systems Group
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Erik Bochinski
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# ---------------------------------------------------------
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from time import time
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import argparse
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from iou_tracker import track_iou
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from util import load_mot, save_to_csv
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def main(args):
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detections = load_mot(args.detection_path)
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start = time()
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tracks = track_iou(detections, args.sigma_l, args.sigma_h, args.sigma_iou, args.t_min)
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end = time()
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num_frames = len(detections)
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print("finished at " + str(int(num_frames / (end - start))) + " fps!")
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save_to_csv(args.output_path, tracks)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="IOU Tracker demo script")
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parser.add_argument('-d', '--detection_path', type=str, required=True,
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help="full path to CSV file containing the detections")
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parser.add_argument('-o', '--output_path', type=str, required=True,
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help="output path to store the tracking results (MOT challenge devkit compatible format)")
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parser.add_argument('-sl', '--sigma_l', type=float, default=0,
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help="low detection threshold")
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parser.add_argument('-sh', '--sigma_h', type=float, default=0.5,
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help="high detection threshold")
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parser.add_argument('-si', '--sigma_iou', type=float, default=0.5,
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help="intersection-over-union threshold")
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parser.add_argument('-tm', '--t_min', type=float, default=2,
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help="minimum track length")
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args = parser.parse_args()
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main(args)

‎mot16.py‎

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#!/usr/bin/env python
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# ---------------------------------------------------------
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# IOU Tracker
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# Copyright (c) 2017 TU Berlin, Communication Systems Group
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Erik Bochinski
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# ---------------------------------------------------------
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from time import time
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import argparse
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from iou_tracker import track_iou
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from util import load_mot, save_to_csv
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def main(args):
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with open(args.seqmap) as fd:
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seqs = [line.rstrip('\n') for line in fd]
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for idx, seq in enumerate(seqs):
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if seq == "name" or seq == "":
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continue
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else:
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det_path = args.benchmark_dir + "/" + seq + "/det/det.txt"
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out_path = args.res_dir + "/" + seq + ".txt"
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detections = load_mot(det_path)
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start = time()
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tracks = track_iou(detections, args.sigma_l, args.sigma_h, args.sigma_iou, args.t_min)
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end = time()
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num_frames = len(detections)
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print("finished " + seq + " at " + str(int(num_frames / (end - start))) + " fps!")
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save_to_csv(out_path, tracks)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="IOU Tracker MOT demo script. Default parameters are set to reproduce "
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"the results using the SDP detections.")
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parser.add_argument('-m', '--seqmap', type=str, required=True,
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help="full path to the seqmap file to evaluate")
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parser.add_argument('-o', '--res_dir', type=str, required=True,
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help="path to the results directory")
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parser.add_argument('-b', '--benchmark_dir', type=str, required=True,
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help="path to the sequence directory")
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parser.add_argument('-sl', '--sigma_l', type=float, default=0.3,
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help="low detection threshold")
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parser.add_argument('-sh', '--sigma_h', type=float, default=0.5,
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help="high detection threshold")
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parser.add_argument('-si', '--sigma_iou', type=float, default=0.3,
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help="intersection-over-union threshold")
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parser.add_argument('-tm', '--t_min', type=float, default=5,
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help="minimum track length")
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args = parser.parse_args()
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main(args)

‎run_tracker.m‎

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function [stateInfo, speed] = run_tracker(curSequence, baselinedetections)
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%% tracker configuration
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%% R-CNN
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sigma_l = 0;
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sigma_h = 0.7;
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sigma_iou = 0.5;
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t_min = 2;
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% %% ACF
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% sigma_l = 0;
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% sigma_h = 0.3;
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% sigma_iou = 0.5;
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% t_min = 3;
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% %% CompACT
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% sigma_l = 0;
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% sigma_h = 0.2;
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% sigma_iou = 0.5;
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% t_min = 2;
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% %% EB
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% sigma_l = 0;
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% sigma_h = 0.8;
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% sigma_iou = 0.5;
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% t_min = 2;
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%% running tracking algorithm
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try
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ret = py.iou_tracker.track_iou_matlab_wrapper(py.numpy.array(baselinedetections(:).'), sigma_l, sigma_h, sigma_iou, t_min);
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catch exception
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disp("error while calling the python tracking module: ")
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disp(" ")
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disp(getReport(exception))
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end
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speed = ret{1};
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track_result = cell2mat(reshape(ret{2}.cell.', 6, []).');
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%% convert and save the mot style track_result
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stateInfo = saveStateInfo(track_result, numel(curSequence.frameNums));

‎saveStateInfo.m‎

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function stateInfo = saveStateInfo(track_result, frame_num)
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try
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sorted_result = sortrows(track_result,6);
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detect_num = size(sorted_result,1);
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catch
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detect_num = 0;
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stateInfo.X(:,1) = zeros(frame_num,1);
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stateInfo.Y(:,1) = zeros(frame_num,1);
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stateInfo.Xi(:,1) = zeros(frame_num,1);
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stateInfo.Yi(:,1) = zeros(frame_num,1);
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stateInfo.W(:,1) = zeros(frame_num,1);
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stateInfo.H(:,1) = zeros(frame_num,1);
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end
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stateInfo.F = frame_num;
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stateInfo.frameNums = 1:frame_num;
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index = 0;
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cur_id = -1;
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for i = 1:detect_num
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if (cur_id ~= sorted_result(i,6))
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cur_id = sorted_result(i,6);
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index = index + 1;
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stateInfo.X(:,index) = zeros(frame_num,1);
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stateInfo.Y(:,index) = zeros(frame_num,1);
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stateInfo.Xi(:,index) = zeros(frame_num,1);
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stateInfo.Yi(:,index) = zeros(frame_num,1);
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stateInfo.W(:,index) = zeros(frame_num,1);
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stateInfo.H(:,index) = zeros(frame_num,1);
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end
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bbox = sorted_result(i,:);
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n = bbox(1,5);
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stateInfo.X(n,index) = bbox(1,1)+0.5*bbox(1,3);
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stateInfo.Y(n,index) = bbox(1,2)+bbox(1,4);
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stateInfo.Xi(n,index) = stateInfo.X(n,index);
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stateInfo.Yi(n,index) = stateInfo.Y(n,index);
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stateInfo.W(n,index) = bbox(1,3);
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stateInfo.H(n,index) = bbox(1,4);
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end

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