| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| - image-to-video |
| language: |
| - en |
| tags: |
| - self-supervised learning |
| - representation learning |
| pretty_name: Walking_Tours |
| size_categories: |
| - n<1K |
| --- |
| |
| <p align="center"style="font-size:32px;"> |
| <strong>Walking Tours Dataset</strong> |
| </p> |
| <p align="center"> |
| <img src="gifs/Wt_img.jpg" alt="Alt Text" width="80%" /> |
| </p> |
|
|
|
|
|
|
| ## Overview |
|
|
| 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. |
|
|
| ## Cities Covered |
|
|
| The dataset encompasses walks through the following cities: |
|
|
| - Amsterdam |
| - Bangkok |
| - Chiang Mai |
| - Istanbul |
| - Kuala Lumpur |
| - Singapore |
| - Stockholm |
| - Venice |
| - Zurich |
|
|
|
|
|
|
| ## Video Specifications |
|
|
| - **Resolution:** 4K (3840 × 2160 pixels) |
| - **Frame Rate:** 60 frames-per-second |
| - **License:** Creative Commons License (CC-BY) |
|
|
| ## Duration |
|
|
| The videos vary in duration, offering a diverse range of content: |
|
|
| - Minimum Duration: 59 minutes (Wildlife safari) |
| - Maximum Duration: 2 hours 55 minutes (Bangkok) |
| - Average Duration: 1 hour 38 minutes |
|
|
|
|
| ## Download the Dataset |
|
|
| The complete list of WTour videos are available in ```WTour.txt```, comprising the YouTube link and the corresponding city. |
|
|
| To download the dataset, we first install **pytube** |
| ``` |
| pip install pytube |
| ``` |
|
|
| then, we run |
| ``` |
| python download_WTours.py --output_folder <path_to_folder> |
| ``` |
|
|
| 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) |
|
|
| To do this for all videos in WTour dataset: |
| ``` |
| python3 -m pip install deface |
| ``` |
| Then run Deface on all videos using the bash script: |
| ``` |
| chmod a+x gdpr_blur_faces.sh |
| ./gdpr_blur_faces.sh |
| ``` |
|
|
|
|
| ## Citation |
|
|
| If you find this work useful and use it on your own research, please cite our paper: |
|
|
| ``` |
| @inproceedings{venkataramanan2023imagenet, |
| title={Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video}, |
| author={Venkataramanan, Shashanka and Rizve, Mamshad Nayeem and Carreira, Jo{\~a}o and Asano, Yuki M and Avrithis, Yannis}, |
| booktitle={International Conference on Learning Representations}, |
| year={2024} |
| } |
| ``` |
| --- |
