| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: Train/** |
| - split: test |
| path: Test/** |
| license: cc-by-4.0 |
| tags: |
| - video |
| - saliency |
| - human |
| - crowdsourcing |
| pretty_name: CrowdSAL |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
|
|
| # CrowdSAL: Video Saliency Dataset and Benchmark |
|
|
|  |
|
|
| ## Dataset |
| [](https://videoprocessing.ai/datasets/crowdsal.html) |
| [](https://drive.google.com/drive/folders/1daH-14w_vHLc9OuGQ_RU0HgUv_Wc3G0o?usp=sharing) |
|
|
| **CrowdSAL** is the largest video saliency dataset with the following key features: |
| * Large scale: **5000** videos with mean **18.4s** duration, **2.7M+** frames; |
| * Mouse fixations from **>19000** observers (**>75** per video); |
| * **Audio** track saved and played to observers; |
| * High resolution: all streams are **FullHD**; |
| * Diverse content from **YouTube, Shorts, Vimeo**; |
| * License: **CC-BY**; |
|
|
| ### File Structure |
|
|
| 1) `Train/Test` folders — dataset splits, ids 0001-3000 are from Train, 3001-5000 from Test subset; |
| |
| 2) `Videos` — 5000 mp4 FullHD, 30 FPS videos with audio streams; |
|
|
| 3) `Saliency` — 5000 mp4 almost losslessly (crf 0, 10bit, min-max normalized) compressed continuous saliency maps videos; |
|
|
| 4) `Fixations` — 5000 json files with per-frame fixation coordinates, from which saliency maps were obtained; |
|
|
| 5) `metadata.jsonl` — meta information about each video (e.g. license, source URL, etc.); |
|
|
|
|
| ## Benchmark Evaluation |
|
|
| [](https://github.com/msu-video-group/CrowdSAL) |
|
|
| ### Environment Setup |
|
|
| ``` |
| conda create -n saliency python=3.10.19 |
| conda activate saliency |
| pip install numpy==2.2.6 opencv-python-headless==4.12.0.88 tqdm==4.67.1 |
| conda install ffmpeg=4.4.2 -c conda-forge |
| ``` |
| ### Run Evaluation |
| Usage example: |
|
|
| 1) Check that your predictions match the structure and names of the Test dataset subset; |
| 2) Install all dependencies from Environment Setup; |
| 3) Download and extract all CrowdSAL files from the dataset page; |
| 4) Run `python bench.py` with flags: |
| * `--model_video_predictions` — folder with predicted saliency videos |
| * `--model_extracted_frames` — folder to store prediction frames (should not exist at launch time) |
| * `--gt_video_predictions` — folder from dataset page with gt saliency videos |
| * `--gt_extracted_frames` — folder to store ground-truth frames (should not exist at launch time) |
| * `--gt_fixations_path` — folder from dataset page with gt saliency fixations |
| * `--mode` — Train/Test subsets split |
| * `--results_json` — path to the output results json |
| 5) The result you get will be available following `results_json` path. |
