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--- |
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license: cc-by-4.0 |
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task_categories: |
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- image-classification |
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- image-to-video |
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language: |
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- en |
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tags: |
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- self-supervised learning |
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- representation learning |
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pretty_name: Walking_Tours |
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size_categories: |
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- n<1K |
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--- |
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<p align="center"style="font-size:32px;"> |
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<strong>Walking Tours Dataset</strong> |
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</p> |
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<p align="center"> |
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<img src="gifs/Wt_img.jpg" alt="Alt Text" width="80%" /> |
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</p> |
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## Overview |
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The Walking Tours dataset is a unique collection of long-duration egocentric videos captured in urban environments from cities in Europe and Asia. It consists of 10 high-resolution videos, each showcasing a person walking through a different environment, ranging from city centers to parks to residential areas, under different lighting conditions. A video from a Wildlife safari is also included to diversify the dataset with natural environments. The dataset is completely unlabeled and uncurated, making it suitable for self-supervised pretraining. |
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## Cities Covered |
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The dataset encompasses walks through the following cities: |
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- Amsterdam |
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- Bangkok |
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- Chiang Mai |
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- Istanbul |
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- Kuala Lumpur |
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- Singapore |
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- Stockholm |
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- Venice |
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- Zurich |
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## Video Specifications |
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- **Resolution:** 4K (3840 × 2160 pixels) |
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- **Frame Rate:** 60 frames-per-second |
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- **License:** Creative Commons License (CC-BY) |
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## Duration |
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The videos vary in duration, offering a diverse range of content: |
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- Minimum Duration: 59 minutes (Wildlife safari) |
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- Maximum Duration: 2 hours 55 minutes (Bangkok) |
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- Average Duration: 1 hour 38 minutes |
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## Download the Dataset |
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The complete list of WTour videos are available in ```WTour.txt```, comprising the YouTube link and the corresponding city. |
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To download the dataset, we first install **pytube** |
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``` |
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pip install pytube |
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``` |
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then, we run |
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``` |
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python download_WTours.py --output_folder <path_to_folder> |
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``` |
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In order to comply with [GDPR](https://gdpr.eu/what-is-gdpr/), we also try to blur out all faces and license plates appearing in the video using [Deface](https://github.com/ORB-HD/deface) |
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To do this for all videos in WTour dataset: |
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``` |
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python3 -m pip install deface |
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``` |
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Then run Deface on all videos using the bash script: |
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``` |
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chmod a+x gdpr_blur_faces.sh |
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./gdpr_blur_faces.sh |
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``` |
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## Citation |
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If you find this work useful and use it on your own research, please cite our paper: |
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``` |
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@inproceedings{venkataramanan2023imagenet, |
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title={Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video}, |
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author={Venkataramanan, Shashanka and Rizve, Mamshad Nayeem and Carreira, Jo{\~a}o and Asano, Yuki M and Avrithis, Yannis}, |
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booktitle={International Conference on Learning Representations}, |
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year={2024} |
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} |
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``` |
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--- |
