bochinski / iou-tracker Goto Github PK

View Code? Open in Web Editor NEW

688.0 31.0 176.0 41 KB

Python implementation of the IOU Tracker

Home Page: http://www.nue.tu-berlin.de

License: MIT License

Python 88.90% MATLAB 6.97% Shell 4.12%

tracker evaluation demo-script mot iou-tracker python detrac ua-detrac tracking-by-detection

iou-tracker's Introduction

IOU Tracker

Python implementation of the IOU/V-IOU Tracker described in the AVSS 2017/2018 papers:

High-Speed Tracking-by-Detection Without Using Image Information

Extending IOU Based Multi-Object Tracking by Visual 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}
}

Install
Demo
DETRAC
Motchallenge
- MOT16
- MOT17
- CVPR19
Visdrone-MOT
Contact

Update (Jan 2020):

added V-IOU Code
updated README.md

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

Install

The repository now contains the code for both the IOU tracker and the V-IOU tracker. The IOU Tracker only depends on numpy while V-IOU also requires OpenCV-Contrib and some other dependencies. It is recommended to use a virtual environment to run the code:

virtualenv -p python3 env
source env/bin/activate
pip install numpy lapsolver tqdm opencv-contrib-python

This should get you started with a basic installation to run most the scripts in this repository.

KCF/KCF2

Two different implementations of the KCF visual tracker can be used. One is supplied by OpenCV-Contrib and is denoted as KCF. This one should work out of the box. The second implementation is denoted as KCF2. This one is needed to reproduce the reported results. In order to use this you need to install KCFcpp-py-wrapper, which is a Cython based wrapper of the original KCF code for python. It is recommended to build and install OpenCV from scratch instead of using the PyPI package in order to have all the necessary headers and libraries available.

Why? Because this implementation seems to work better and much faster than the one provided by OpenCV-Contrib.

If you do not install this module the tracker will automatically fall back on the OpenCV implementation (i.e. KCF) and a warning is printed. You can get rid of this warning by either by installing the other KCF module as described above or explicitly request KCF instead of KCF2 in the scripts. Note that the tracking performance will be affected by this.

Demo

Several demo scripts are included to reproduce the reported results on the UA-DETRAC , MOT 16/17/19 and VisDrone benchmarks.

Basic demo script (not dataset specific, can be used for other applications):

$ ./demo.py -h
usage: demo.py [-h] [-v VISUAL] [-hr KEEP_UPPER_HEIGHT_RATIO] [-f FRAMES_PATH]
               -d DETECTION_PATH -o OUTPUT_PATH [-sl SIGMA_L] [-sh SIGMA_H]
               [-si SIGMA_IOU] [-tm T_MIN] [-ttl TTL] [-nms NMS] [-fmt FORMAT]

IOU/V-IOU Tracker demo script

optional arguments:
  -h, --help            show this help message and exit
  -v VISUAL, --visual VISUAL
                        visual tracker for V-IOU. Currently supported are
                        [BOOSTING, MIL, KCF, KCF2, TLD, MEDIANFLOW, GOTURN,
                        NONE] see README.md for furthert details
  -hr KEEP_UPPER_HEIGHT_RATIO, --keep_upper_height_ratio KEEP_UPPER_HEIGHT_RATIO
                        Ratio of height of the object to track to the total
                        height of the object for visual tracking. e.g. upper
                        30%
  -f FRAMES_PATH, --frames_path FRAMES_PATH
                        sequence frames with format
                        '/path/to/frames/frame_{:04d}.jpg' where '{:04d}' will
                        be replaced with the frame id. (zero_padded to 4
                        digits, use {:05d} for 5 etc.)
  -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/Visdrone 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
  -ttl TTL, --ttl TTL   time to live parameter for v-iou
  -nms NMS, --nms NMS   nms for loading multi-class detections
  -fmt FORMAT, --format FORMAT
                        format of the detections [motchallenge, visdrone]

Example for the IOU tracker on 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

Example for the V-IOU tracker on the uav0000137_00458_v Visdrone sequence:

demo.py -f '/path/to/VisDrone2018-MOT-val/sequences/uav0000137_00458_v/{:07d}.jpg' -d /path/to/VisDrone2018-MOT-val/detections/uav0000137_00458_v.txt -o results/VisDrone2018-MOT-val/uav0000137_00458_v.txt -v MEDIANFLOW -sl 0.9 -sh 0.98 -si 0.1 -tm 23 --ttl 8 --nms 0.6 -fmt visdrone

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. You can switch between IOU and V-IOU and select the different parameters for different detectors in run_tracker.m

Note that you still need a working python environment with numpy for IOU and all other dependencies for V-IOU installed. You should obtain something like the following results for the 'DETRAC-Train' set:

DETRAC-Train Results

IOU Tracker:

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

V-IOU Tracker:

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
CompACT	26.84	49.57	0.10	19.63	14.67	17.39	8750.71	143532.90	244.98	444.20	25.29	41.58	25.33
Mask R-CNN	42.80	47.50	0.60	38.32	8.36	26.24	50294.76	174052.00	448.16	293.69	34.02	46.87	34.10

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).

Motchallenge

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.

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

CVPR19

To reproduce the results on the CVPR19 dataset you can use the cvpr19.sh bash script.

Edit the first lines according to your setup:

# set these variables according to your setup
seq_dir=/path/to/cvpr19/train # base directory of the split (cvpr19/train, cvpr19/test etc.)
results_dir=results/cvpr19    # output directory, will be created if not existing

Then, run (The used parameters for the demo.py script will be displayed for your convenience):

./cvpr19.sh

Note that this requires to have KCF2 tracker installed.

Note that only the upper 30% of the detections are used for visual tracking since the bottom part is often occluded. Due to time constraints only KCF2 was evaluated for the CVPR19 challenge participation, MEDIANFLOW might yield better results like for the Visdrone-MOT experiments but additional parameter tuning is required.

CVPR19 Train Results

Detector	IDF1	IDP	IDR	Rcll	Prcn	FAR	GT	MT	PT	ML	FP	FN	IDs	FM	MOTA	MOTP	MOTAL
FRCNN	53.9	71.6	43.2	59.9	99.3	0.63	2274	670	1187	417	55844	97170	3272	4792	59.2	87.5	59.5

Visdrone-MOT

To reproduce the results on the Visdrone MOT dataset you can use the visdrone-mot.sh bash script. Edit the first lines according to your setup:

# set these variables according to your setup
visdrone_dir=/path/to/VisDrone2018-MOT-val  # base directory of the split (VisDrone2018-MOT-val, VisDrone2018-MOT-train etc.)
results_dir=results/VisDrone2018-MOT-val    # output directory, will be created if not existing
vis_tracker=MEDIANFLOW                      # [MEDIANFLOW, KCF2, NONE] parameter set as used in the paper

Then, run (The used parameters for the demo.py script will be displayed for your convenience):

./visdrone-mot.sh

Visdrone-MOT Val Results

For the VisDrone2018-MOT-val split you should get the following results:

Visual Tracker	IDF1	IDP	IDR	Rcll	Prcn	FAR	GT	MT	PT	ML	FP	FN	IDs	FM	MOTA	MOTP	MOTAL
None	40.9	68.5	29.2	34.6	81.3	0.60	476	102	76	297	5736	46979	177	435	26.4	78.1	26.6
KCF	45.3	75.3	32.4	35.2	81.8	0.59	476	105	64	305	5605	46578	76	385	27.3	77.9	27.4
Medianflow	45.6	75.9	32.6	35.3	82.2	0.58	476	107	63	304	5494	46466	65	378	27.6	77.8	27.7

Contact

If you have any questions or encounter problems regarding the method/code feel free to contact me at [email protected]

iou-tracker's People

Contributors

Stargazers

Watchers

Forkers

kingmv rizhiy gchen2016 zhyj3038 dreadlord1984 themikeyr jslslee liuyutingliuyuting geoffgao haoli-buaa grabber gbjim abrams90 signalimagecv jeffshih ddwapple cw1204772 rkshuai starstylesky shuoyuanxu hengshan123 december-boy yhl2018 zebrajack hyfine vinodhian matrixping dl-85 fangmingbnu kranthisai liujiandu jiniljoseph w510056105 huwenyue dishen12 nyk510 dexception syeda27 smzcc remgao siyuanc2 songsanling nuggy875 horaccefeng xmichaelx happyuncle tsenst junshix liuliu408 willamezhang denethor1997 bingkunj okanlv alexblack2202 saeedarabi92 junweston adam-dziedzic ted8201 hodenx juanlp andeyeluguo arishin sga8 trantorrepository danhlephuoc linxueya arasharchor bigboy-code nuanxinqing darrenruan onepunchmanmxt traseehq abhishek-bhardwaj-in nhaplycafedang alex4men phongphuhanam bityangke zpbeibei hnjm mirapurkrabek j1nwoopark nangchoo olegjakushkin wyjforwjy miczyg1395 lionelleee civilpat zongweizhou1 czzlegend sailingzhuang idmakers chuong98 hengzhangmiivii xiaoye77 jingziyou akangman33 yanfg1 littlecarrotz cig2982 fegonda

iou-tracker's Issues

DETRAC-toolkit is not available

Hi!
I would like to use DETRAC-toolkit, but it's no longer available (on https://detrac-db.rit.albany.edu/download).
Could you please tell me where to download it? Or other alternatives how to test your tracker on DETRAC dataset.
Thank you a lot

problem about the reproduce of MOTCVPR19 results

running error

we need install KCF tracker before run VIOU?

Unable to pip install lapsolver

Hi, everyone! I want to run this code and I create a vitrual environment. But I can't install the lapsolver with the pip. Have any of you been in this situation? Thanks a lot.

Problem about reproduce the mot17 result

hi nice work!
I want to reproduce the mot17 track results. the result of mot17 FRCNN in README.txt is the average result of all FRCNN? because there are many FRCNN gt and det.

detections for vis-drone dataset

Hi,
Can u provide the detections for vis-drone dataset?

Problem about the

Unable to produce the repository results based upon v-iou tracker for visdrone dataset

Hi,
I am unable to produce the results for visdrone-mot-val dataset based upon v-iou as reported in the repository. Procedures , I am following, are:

Run the "./visdrone-mot.sh" command and it produces separate csv(equivalent to number of input sequences) files based upon v-iou tracker. I dont know where to give these cvs files as input for the producing the mentioned results. Can any one guide please? Visdrone toolkit has no option to give these files as input. DETRAC toolkit is not available to download.
Also, I downloaded the Visdrone-mot-toolkit for the evaluation. I placed the v-iou tracker files in the ./tracker folder of the toolkit. I run the "run_tracker.m" file from the matlab to produce the results but following error is produced every time. Can anyone able to resolve this issue in producing the repository results for visdrone dataset. I shall be thankful.
Kind Regards,

tracking the sequence 1/6...

error while calling the python tracking module:

Error using viou_tracker>track_viou_matlab_wrapper (line 220)
Python Error: ValueError: cannot reshape array of size 138720 into shape (7,newaxis)

Error in runTrackerAllClass (line 32)
[resClass, runTime] = run_tracker(sequence, curdetections);

Error in evalMOT (line 29)
runTrackerAllClass(isSeqDisplay, isNMS, detPath, resPath, seqPath, evalClassSet, threSet, nmsThre, trackerName);
Error using runTrackerAllClass (line 44)
error in running the tracker!

Error in evalMOT (line 29)
runTrackerAllClass(isSeqDisplay, isNMS, detPath, resPath, seqPath, evalClassSet, threSet, nmsThre,
trackerName);

running error

TypeError: integer argument expected, got float

how to predict next bbox and reid？

Reproducing Results on DETRAC-Train Dataset with Mask-RCNN

Hello,

I was trying to reproduce your results for the IOU-tracker on DETRAC-Train using detector Mask-RCNN. Although I have used the provided detection results and a new clone of your tracker, I keep getting results with MOTA of about 29.4 as opposed to the reported value of 34.36.
Tracker iou-tracker-master + Detector frcnn by DETRAC-MOT Evaluation:
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
34.8 41.0 0.36|28.8% 11.7% 26.7%|30139.2 187059.4 1006.6 995.2| 29.4 38.4 29.6
Would you mind looking at my attached logs below and see if I did anything wrong? Thank you very much for your help!
log.txt

detections numpy array（fieldNames = ['frame','id','x','y','w','h',score]）, What does ‘id’ mean？

detections path to csv file containing the detections or numpy array containing them.
Loads detections stored in a mot-challenge like formatted CSV or numpy array containing them.

numpy array（fieldNames = ['frame','id','x','y','w','h','score']）, What does ‘id’ mean？

Thank you for your reply

Can i test the model with real time custom data ?

please help , where i need to modify for the custom data loading and how to visualize the results ??

load_mot() no longer adapts to MOT17 det.txt files

Hi,
First of all, great work. Thanks for making it available to the public.

It seems that MOT17 datasets have changed since the code for load_mot() in util.py was written. Now the sequences from detectors FRCNN and SDP only provide det.txt files with 7 columns (no information for classes) and load_mot() fails here.

I fixed it by calling load_mot() in mot17.py with flag with_classes=False. Just wanted to let you know to check that the fix is ok and to suggest to update these demos accordingly, if you reckon appropiate.

Detections for visdrone dataset and common line arguments

Hi,
Thanks to write this repo for objects tracking using both IoU and V-IoU. Can you please solve following queries:

I am unable to get the visdrone detections file to test the mot tracking.
Also, which arguments to use on command line to generate the mot-visdrone results?
Thanks in advance for your kind response.
Kind Regards,

Input format to the tracker

Hi, what is the format of the input to the tracker?
is it:
frame bbleft bbtop bbwidth bbheight confidence
?

I am not using any of the standard MOT detections.
thanks.

V-IOU tracker improvements implementation ?

Hello,

Is the v-iou implementation available somewhere ? (based on paper Extending IOU Based Multi-Object Tracking by Visual Information)

I'm doing something close to V-IOU idea in a node.js implementation: https://github.com/tdurand/node-moving-things-tracker but in a much less "academic" way.

There is not much details about the "prediction" method you use for the visual tracking.. On my project I'm using velocity / acceleration vectors but do not change the bounding box width / height

From you paper, I'm gonna try this idea of searching "back-wards through the last ttl frames for each new track" , this is something I'm not doing currently

Thanks !

Thibault

could you modified your code for tracking on next frame while not all frames?

thanks

Visualize results

Hi,

Have you added a script to visualize the results ?

Few questions

Question 1: How this the algorithm behave in a scenario with two tracks of two persons, where the detector fails to detect one person in one frame but manage to detect the person in the next frame. Will it recover the track or simple assign it a new trackid?

Question 2: Any plans to make an online version of this algorithm (like SORT) ?

how to get test results like pr-mota on UA-DETRAC

hi, thanks for your work! Could you please tell me how to get the test results like pr-mota on UA-DETRAC? Now the website of UA-DETRAC is could not to submit results.

eval tracker

I'm new to tracker. No knowledge of the data set used in the evaluation.
Why do you need to use the training data set when evaluating?
and test data not have gt.txt, how using test data to eval tracker?

Finish tracker just after a few frames

I quite often loose some tracks because of missing detections in a single or two frames.

I added a "minimum age" criteria to the finishing track and save the tracks for one or two frames.

This will propably just work with high framerates of about 20fps and more.

adding a track['age'] = 0 right before the updated_tracks.append(track) in line 45 for matched tracks and changing line 52 if track['max_confidence'] >= sigma_h and (track['frames']) >= t_min: to

if track['age'] < 2:
  track['bboxes'].append(track['bboxes'][-1])
  track['age'] += 1
  updated_tracks.append(track)
  track['max_confidence'] >= sigma_h and (track['frames']) >= (t_min + track['age']):
...

will do it.

Somebody could prove that? Am I missing something?

CVPR19 MOT Data

Hello,

First of all, thank you for posting the V-IOU code :)
I would like to reproduce the results that you've obtained on CVPR19 MOT Data but I can't seem to find where to download it from.

The links https://motchallenge.net/data/CVPR_2019_Tracking_Challenge/ and https://motchallenge.net/data/CVPR_2019_Detection_Challenge/ are not working.

Do you happen to have the data? If yes, could you please drop me a link to download it?

Thanks a lot,
Nicolae

Recommend Projects

React

A declarative, efficient, and flexible JavaScript library for building user interfaces.
Vue.js

🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript

TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
TensorFlow

An Open Source Machine Learning Framework for Everyone
Django

The Web framework for perfectionists with deadlines.
Laravel

A PHP framework for web artisans
D3

Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

javascript

JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
web

Some thing interesting about web. New door for the world.
server

A server is a program made to process requests and deliver data to clients.
Machine learning

Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Visualization

Some thing interesting about visualization, use data art
Game

Some thing interesting about game, make everyone happy.

Recommend Org

Facebook

We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft

Open source projects and samples from Microsoft.
Google

Google ❤️ Open Source for everyone.
Alibaba

Alibaba Open Source for everyone
D3

Data-Driven Documents codes.
Tencent

China tencent open source team.

bochinski / iou-tracker Goto Github PK

iou-tracker's Introduction

IOU Tracker

Table Of Contents

Install

KCF/KCF2

Demo

DETRAC

DETRAC-Train Results

DETRAC-Test (Overall) Results

EB detections

Mask R-CNN detections

Motchallenge

MOT16

MOT16 Train Results

MOT16 Test Results

MOT17

MOT17 Train Results

MOT17 Test Results

CVPR19

CVPR19 Train Results

Visdrone-MOT

Visdrone-MOT Val Results

Contact

iou-tracker's People

Contributors

Stargazers

Watchers

Forkers

iou-tracker's Issues

Error in evalMOT (line 29) runTrackerAllClass(isSeqDisplay, isNMS, detPath, resPath, seqPath, evalClassSet, threSet, nmsThre, trackerName);

Recommend Projects

Recommend Topics

Recommend Org

Error in evalMOT (line 29)
runTrackerAllClass(isSeqDisplay, isNMS, detPath, resPath, seqPath, evalClassSet, threSet, nmsThre,
trackerName);