• Stars
    star
    222
  • Rank 179,123 (Top 4 %)
  • Language
    Python
  • License
    MIT License
  • Created over 5 years ago
  • Updated over 3 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Tools for evaluating and visualizing results for the Multi Object Tracking and Segmentation (MOTS) task

mots_tools

Tools for evaluating and visualizing results for the Multi Object Tracking and Segmentation (MOTS) task.

For the TrackR-CNN code please visit https://github.com/VisualComputingInstitute/TrackR-CNN

Project website (including annotations)

https://www.vision.rwth-aachen.de/page/mots

Paper

https://www.vision.rwth-aachen.de/media/papers/mots-multi-object-tracking-and-segmentation/MOTS.pdf

Using the mots_tools

Please install the cocotools (https://github.com/cocodataset/cocoapi), which we use with run-length encoded binary masks. If you want to visualize your results using this script, please also install FFmpeg.

In order to evaluate or visualize the results of your MOTS method, please export them in one of the two formats we use for the ground truth annotations: png or txt (see https://www.vision.rwth-aachen.de/page/mots). When using png, we expect the result images to be in subfolders corresponding to the sequences (e.g. tracking_results/0002/000000.png, tracking_results/0002/000001.png, ...). When using txt, we expect filenames corresponding to the sequences (e.g. tracking_results/0002.txt, tracking_results/0006.txt, ...).

Evaluating a tracking result

Clone this repository, navigate to the mots_tools directory and make sure it is in your Python path. Now suppose your tracking results are located in a folder "tracking_results". Suppose further the ground truth annotations are located in a folder "gt_folder". Then you can evaluate your results using the commands

python mots_eval/eval.py tracking_results gt_folder seqmap

where "seqmap" is a textfile containing the sequences which you want to evaluate on. Several seqmaps are already provided in the mots_eval repository: val.seqmap, train.seqmap, fulltrain.seqmap, val_MOTSchallenge.seqmap which correspond to the KITTI MOTS validation set, the KITTI MOTS training set, both KITTI MOTS sets combined and the four annotated MOTSChallenge sequences respectively.

Parts of the evaluation logic are built upon the KITTI 2D tracking evaluation devkit from http://www.cvlibs.net/datasets/kitti/eval_tracking.php

Visualizing a tracking result

Similarly to evaluating tracking results, you can also create visualizations using

python mots_vis/visualize_mots.py tracking_results img_folder output_folder seqmap

where "img_folder" is a folder containing the original KITTI tracking images (http://www.cvlibs.net/download.php?file=data_tracking_image_2.zip) and "output_folder" is a folder where the resulting visualization will be created.

Citation

If you use this code, please cite:

@inproceedings{Voigtlaender19CVPR_MOTS,
 author = {Paul Voigtlaender and Michael Krause and Aljo\u{s}a O\u{s}ep and Jonathon Luiten and Berin Balachandar Gnana Sekar and Andreas Geiger and Bastian Leibe},
 title = {{MOTS}: Multi-Object Tracking and Segmentation},
 booktitle = {CVPR},
 year = {2019},
}

License

MIT License

Contact

If you find a problem in the code, please open an issue.

For general questions, please contact Paul Voigtlaender ([email protected]) or Michael Krause ([email protected])

More Repositories

1

triplet-reid

Code for reproducing the results of our "In Defense of the Triplet Loss for Person Re-Identification" paper.
Python
764
star
2

TrackR-CNN

TrackR-CNN baseline method for Multi-Object Tracking and Segmentation (MOTS)
Python
520
star
3

diffusion-e2e-ft

Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Python
275
star
4

SiamR-CNN

Siam R-CNN two-stage re-detector for visual object tracking
Python
218
star
5

2D_lidar_person_detection

Person detector for 2D range data. Code release for Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera (https://arxiv.org/abs/2012.08890)
Python
161
star
6

vkitti3D-dataset

Python
100
star
7

3d-semantic-segmentation

This work is based on our paper Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds, which is appeared at the IEEE International Conference on Computer Vision (ICCV) 2017, 3DRMS Workshop.
Python
98
star
8

towards-reid-tracking

Code for the paper "Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters"
Python
82
star
9

DR-SPAAM-Detector

DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data
Python
78
star
10

ShapePriors_GCPR16

C++
47
star
11

DROW

All code related to the "DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data" paper
Jupyter Notebook
41
star
12

Person_MinkUNet

Person-MinkUNet. Winner of JRDB 3D detection challenge in JRDB-ACT Workshop at CVPR 2021. https://arxiv.org/abs/2107.06780
Python
20
star
13

BiternionNets-ROS

An implementation of BiternionNets for ROS, ready to run on a robot.
Python
13
star
14

mots_trackingonly_tools

Tools for Challenge 3: Tracking Only (MOT+KITTI) of MOTChallenge 2020
Python
8
star
15

Beacon8

A Torch-inspired library for high-level deep learning with Theano.
Python
5
star
16

RovinaSemanticSegmentation

Semantic segmentation code for the ROVINA project.
C++
4
star
17

CROWDBOT_perception

This is the perception pipeline for the CROWDBOT project, featuring person detection and tracking from multi-sensor modalities.
C
3
star
18

cityscapes-util

Utility toolbox for dealing with the CityScapes dataset.
Python
3
star
19

omni3d-rgbd

2
star
20

PARIS-sem-seg

A straight forward network for semantic segmentation in TensorFlow
Python
2
star
21

ROS-laserdumper

Dump ROS LaserScan data into csv files.
C++
1
star