| --- |
| pretty_name: IRVAL |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - video-to-video |
| tags: |
| - infrared |
| - thermal |
| - lwir |
| - video |
| - computer-vision |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: IRVAL/videos/*.avi |
| --- |
| |
| # IRVAL |
|
|
| IRVAL is a high-resolution infrared video dataset for infrared video processing and spatial-temporal video super-resolution research. |
|
|
| ## Dataset Summary |
|
|
| This repository currently provides 8 infrared videos in `.avi` format. |
|
|
| According to our associated paper, IRVAL is a high-resolution infrared dataset comprising 108,512 video frames at a spatial resolution of 512×512. The data are collected using vanadium oxide (VOx) uncooled focal plane array detectors operating in the long-wave infrared (LWIR) band. The videos are captured from both vehicle-mounted and fixed surveillance platforms, covering real-world scenarios such as urban streets, vehicles, pedestrians, and roadside buildings. |
|
|
| ## Repository Structure |
|
|
| ```text |
| . |
| ├── README.md |
| ├── IRVAL/ |
| │ └── videos/ |
| │ ├── irval_seq01.avi |
| │ ├── irval_seq02.avi |
| │ ├── irval_seq03.avi |
| │ ├── irval_seq04.avi |
| │ ├── irval_seq05.avi |
| │ ├── irval_seq06.avi |
| │ ├── irval_seq07.avi |
| │ └── irval_seq08.avi |
| └── .gitattributes |
| ``` |
|
|
| ## Intended Use |
|
|
| This dataset is intended for research on: |
|
|
| - infrared video processing |
| - infrared video super-resolution |
| - spatial-temporal video super-resolution |
| - temporal consistency modeling |
|
|
| ## Notes |
|
|
| - This release currently contains 8 raw infrared videos. |
| - The current video path used by the dataset viewer is `IRVAL/videos/*.avi`. |
| - Users can generate task-specific LR/HR training pairs following their own protocol or the protocol described in the associated paper. |
| - This dataset is intended for research use only. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @inproceedings{zhou2026thermal, |
| title={Thermal Diffusion Matters: Infrared Spatial-Temporal Video Super-Resolution through Heat Conduction Priors}, |
| author={Mingxuan Zhou and Shuang Li and Yutang Zhang and Jing Geng and Yirui Shen and Jingxuan Kang and Fuzhen Zhuang and Shuigen Wang}, |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
| year={2026} |
| } |
| ``` |
