iou-tracker: A Python repository from Alebat

IOU Tracker

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},
	}

@INPROCEEDINGS{1547Bochinski2018,
	AUTHOR = {Erik Bochinski and Tobias Senst and Thomas Sikora},
	TITLE = {Extending IOU Based Multi-Object Tracking by Visual Information},
	BOOKTITLE = {IEEE International Conference on Advanced Video and Signals-based Surveillance},
	YEAR = {2018},
	MONTH = nov,
	PAGES = {441--446},
	ADDRESS = {Auckland, New Zealand},
	URL = {http://elvera.nue.tu-berlin.de/files/1547Bochinski2018.pdf}
}

Update (December 2018):

added V-IOU results of our new paper Extending IOU Based Multi-Object Tracking by Visual Information
Mask R-CNN detections for UA-DETRAC added
CompACT parameters improved

Demo

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

DETRAC

To reproduce the reported results, download and extract the DETRAC-toolkit and the detections you want to evaluate. Download links for the EB and Mask R-CNN 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:

DETRAC-Train Results

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	25.20	49.69	0.10	18.50	14.11	19.06	8053.54	153026.99	2021.84	2302.83	23.46	42.96	23.81
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
Mask R-CNN	43.21	47.26	0.60	37.22	11.46	24.24	50096.88	171714.09	1021.94	929.53	34.36	45.43	34.54

DETRAC-Test (Overall) Results

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
IOU	CompACT	16.1%	37.0%	14.8%	19.7%	2308.1	3250.4	24349.4	176752.8	327,660 fps
IOU	Mask R-CNN	30.7%	37.0%	30.3%	21.5%	668.0	733.6	17370.3	179505.9	14,956 fps
V-IOU	CompACT	17.7%	36.4%	17.4%	18.8%	363.8	1123.5	26413.3	166571.7	1117.90fps
V-IOU	Mask R-CNN	30.7%	37.0%	32.0%	22.6%	162.6	286.2	18046.2	179191.2	359.18 fps

EB detections

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:

Mask R-CNN detections

These detections are generated using a recent Mask R-CNN implementation trained on COCO. Only bounding boxes for COCOs car, bus and truck classes are included. Note that the detector is called "frcnn" (use options.detectorSet = {'frcnn'}; in initialize_environment.m).

MOT17

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

MOT17 Train Results

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

MOT17 Test Results

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

MOT16

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:

MOT16 Train Results

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

MOT16 Test Results

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.

Contact

If you have any questions or encounter problems regarding the method/code feel free to contact me at bochinski@nue.tu-berlin.de

Alebat/iou-tracker