| # EPIC-KITCHENS-100 Dataset - Extension Video Release | |
| Release Date: May 2020 | |
| ## Authors | |
| Dima Damen (1) | |
| Hazel Doughty (1) | |
| Giovanni Maria Farinella (2) | |
| Antonino Furnari (2) | |
| Evangelos Kazakos (1) | |
| Jian Ma (1) | |
| Davide Moltisanti (1) | |
| Jonathan Munro (1) | |
| Toby Perrett (1) | |
| Will Price (1) | |
| Michael Wray (1) | |
| * (1 University of Bristol) | |
| * (2 University of Catania) | |
| ## Citing | |
| When using the dataset, kindly reference: | |
| Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray (2020). Rescaling Egocentric Vision. check publication [here](http://epic-kitchens.github.io) | |
| Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray (2020). The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). | |
| Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray (2018). Scaling Egocentric Vision: The EPIC-KITCHENS Dataset. European Conference on Computer Vision (ECCV). | |
| ## Dataset Details | |
| This deposit contains additional videos for the dataset EPIC-KITCHENS-100. These contain 45 recorded hours, and together with videos released at: http://dx.doi.org/10.5523/bris.3h91syskeag572hl6tvuovwv4d form a total of 100 hours of egocentric footage, by 37 participants. | |
| This readme contains information about additionally recorded video files. Please see the | |
| [github](https://github.com/epic-kitchens/annotations) for the latest annotations | |
| and [epic-kitchens website](http://epic-kitchens.github.io) for all and open challenges. | |
| ## Folder Details | |
| We have a folder per participant PXX (e.g. P01). Within each folder, you can find the following sub-folders: | |
| * **videos:** contains the raw videos. | |
| We use `P##/P##_1**` with `##` denoting the participant number and `1**` to denote | |
| the video number. We use 1 to indicate an extension video compared to videos released in 2018. | |
| Videos are recordied using the GoPro Hero 7 at 50 FPS with stabilisation. | |
| * **rgb_frames:** contains the RGB frames extracted at 50fps. | |
| Frames from a video have been grouped into a tar file, labelled as `P##_***.tar` | |
| and can be found inside the participant directory, i.e. The RGB frames from, | |
| `P01_101` can be found in `rgb_frames/P01_101.tar`. Each tar file contains a flat directory | |
| of frames named `frame_xxxxxxxxxx.jpg`. | |
| RGB frames are extracted using the command | |
| ``` | |
| ffmpeg \ | |
| -hwaccel cuvid \ | |
| -c:v "hevc_cuvid" \ | |
| -i PXX_1YY.MP4 \ | |
| -vf 'scale_npp=-2:256:interp_algo=super,hwdownload,format=nv12' \ | |
| -q:v 4 \ | |
| -r 50 \ | |
| PXX_1YY/frame_%010d.jpg | |
| ``` | |
| * **flow_frames:** contains the flow frames used as input for action recognition | |
| Similar to rgb_frames, flow_frames are grouped into a tar file, and follow the same formatting. | |
| Optical flow was extracted using a fork of | |
| [`gpu_flow`](https://github.com/feichtenhofer/gpu_flow) made | |
| [available on github](https://github.com/dl-container-registry/furnari-flow). | |
| We set the parameters: stride = 1, dilation = 1, bound = 8 and size = 256. | |
| * **meta_data:** | |
| We publish meta data containing accelerometers and gyroscope data. These have been collected using the public [GoPro-utilities repo](https://github.com/JuanIrache/gopro-utils). Please refer to that repo for the formats of the released meta_data files. Note that GPS information is suppressed for anonymity and were not recorded. | |
| * **recording_times.csv** | |
| This file contains the exact recording time for each file (in date, hours and minutes), based on the local time at the city/country of recording. | |
| ## License | |
| All files in this dataset are copyright by us and published under the | |
| Creative Commons Attribution-NonCommerial 4.0 International License, found | |
| [here](https://creativecommons.org/licenses/by-nc/4.0/). | |
| This means that you must give appropriate credit, provide a link to the license, | |
| and indicate if changes were made. You may do so in any reasonable manner, | |
| but not in any way that suggests the licensor endorses you or your use. You | |
| may not use the material for commercial purposes. | |