VIZTA Time of Flight Dataset and Benchmark
VIZTA aims at developing innovative technologies in the field of optical sensors and laser sources for short to long-range 3D-imaging and to demonstrate their value in several key applications including automotive, security, smart buildings, mobile robotics for smart cities, and industry 4.0. The VIZTA project also includes the development of demonstrators for these applications.
DFKI provides two time-of-flight datasets within the scope of this project to facilitate the development and evaluation of algorithms for two of the key use cases which are in-cabin monitoring and smart building management. For both use cases the dataset provides annotated data to train and evaluate deep learning algorithms for the detection and segmentation of persons and objects in 2D and 3D space.
Our in-car cabin dataset TICaM shall facilitate the realization of a robust object and person detection on the vehicle front seats. In addition, ground truth annotation for person activity recognition is provided. These algorithms shall be the basis for the realization of various in-cabin monitoring functions comprising active and passive safety functions as well as comfort functions and advanced human-vehicle interfaces for future autonomous and driver-less vehicles.
Our TIMo dataset provides frame-wise person annotation in 2D and 3D also annotations of anomalies for indoor scenes. In this way the dataset shall facilitate the realization of highly accurate people counting as well as security functions like access control to critical areas and anomalous behavior detection.
More information on the VIZTA project can be found here.
Publications
If you would like to use our TICaM or TIMo dataset, please cite the appropriate publication:
@inproceedings{katrolia2021ticam,
author = {Jigyasa Singh Katrolia and
Ahmed El{-}Sherif and
Hartmut Feld and
Bruno Mirbach and
Jason R. Rambach and
Didier Stricker},
title = {TICaM: {A} Time-of-flight In-car Cabin Monitoring Dataset},
booktitle = {32nd British Machine Vision Conference 2021, {BMVC} 2021, Online,
November 22-25, 2021},
pages = {277},
publisher = {{BMVA} Press},
year = {2021},
url = {https://www.bmvc2021-virtualconference.com/assets/papers/0701.pdf},
timestamp = {Wed, 22 Jun 2022 16:52:45 +0200},
biburl = {https://dblp.org/rec/conf/bmvc/KatroliaEFMRS21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}Direct link to the TICaM paper: TICaM Paper
@Article{schneider2022timo,
AUTHOR = {Schneider, Pascal and Anisimov, Yuriy and Islam, Raisul and Mirbach, Bruno and Rambach, Jason and Stricker, Didier and Grandidier, Frédéric},
TITLE = {TIMo—A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera},
JOURNAL = {Sensors},
VOLUME = {22},
YEAR = {2022},
NUMBER = {11},
ARTICLE-NUMBER = {3992},
URL = {https://www.mdpi.com/1424-8220/22/11/3992},
PubMedID = {35684612},
ISSN = {1424-8220},
DOI = {10.3390/s22113992}
}Direct link to the TIMo paper: TIMo Paper
License
This work, all the datasets and benchmarks are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This means that:
- You must give appropriate credit.
- You may not use the material for commercial purposes.
- If you remix, transform or build upon the material, you must distribute your contributions under the same license as the original.
Acknowledgement
This work was partially funded within the Electronic Components and Systems for European Leadership (ECSEL) Joint Undertaking in collaboration with the European Union’s H2020 Framework Program and Federal Ministry of Education and Research of the Federal Republic of Germany (BMBF), under grant agreement 16ESE0424 / GA826600 (VIZTA).
News
22.10.2024: Test labels for TiCAM added.
29.09.2021: Synthetic point clouds in TICaM dataset updated.
30.08.2021: The TIMo paper is now available on arXiv
26.08.2021: The TIMo dataset is now released to the public
30.06.2021: Driver activity label ‘turn to forward’ removed.
15.06.2021: 2D keypoint annotations added for synthetic imageset.
26.04.2021: ‘Infant’ and ‘Child’ are now their own classes in synthetic dataset. Earlier they were labeled as ‘Person’.
12.04.2021: 4 RGB videos and 222 corresponding RGB frames are removed from the real test dataset.
24.02.2021: VIZTA Time-of-flight website is launched.