Datasets:

ANDRYHA
/

SAVAM

	---
	pretty_name: SAVAM — Semiautomatic Visual-Attention Modeling
	configs:
	- config_name: clips
	default: true
	data_files:
	- split: train
	path: viewer/clips/*.parquet
	- config_name: filtered_gaze
	data_files:
	- split: train
	path: viewer/filtered_gaze/*.parquet
	- config_name: raw_gaze
	data_files:
	- split: train
	path: viewer/raw_gaze/*.parquet
	tags:
	- saliency
	- video
	- fixations
	- gaze
	size_categories:
	- n<1K
	---

	# SAVAM — Semiautomatic Visual-Attention Modeling

	Official dataset page: [videoprocessing.ai/datasets/savam.html](https://videoprocessing.ai/datasets/savam.html)

	SAVAM contains human eye-movement recordings collected while viewing videos, including static and dynamic scenes, film excerpts, and sequences from research video databases.

	## Official dataset description

	- 41 video fragments from feature films, commercials, and stereoscopic video databases.
	- Approximately 13 minutes of video, ~20000 frames.
	- 50 observers, predominantly aged 18–27.
	- Full HD and 4K UHDTV stereoscopic video sequences.
	- Eye tracking with an SMI iViewX Hi-Speed 1250 at 500 Hz.
	- Additional postprocessing to improve recording accuracy.

	## Gaze data

	The descriptions below follow the original [`GazeData/README.txt`](GazeData/README.txt).

	\| Directory \| Contents \|
	\| --- \| --- \|
	\| `GazeData/raw_gaze_data/` \| Original eye-tracking device output \|
	\| `GazeData/filtered_gaze_data/` \| Filtered eye-tracking data \|
	\| `GazeData/gaussian_vizualizations/` \| Gaze-location distributions visualized with multiple Gaussians \|

	The original processing procedure described in the [Data post-processing section](https://videoprocessing.ai/datasets/savam.html#data-post-processing) of the official page.

	### Metadata

	- [`list_video.txt`](GazeData/list_video.txt): video name, source sequence, and starting frame in that sequence.
	- [`list_user.txt`](GazeData/list_user.txt): observer identifier, sex (`m` or `f`), age, viewing number, and presentation order (`bwd` or `fwd`).
	- Gaze filenames contain the video name, source sequence, starting frame, observer identifier, sex, age, trial number, and presentation order.

	Some observers participated more than once. For `bwd` recordings, the order of clips was reversed, not the order of frames within a clip. The original documentation states that these data do not need to be reversed.

	### Gaze coordinates

	The original format documentation lists the following fields:

	\| Position \| Field \|
	\| --- \| --- \|
	\| 1 \| Timestamp; 1,000,000 units correspond to one second \|
	\| 2 \| Left X coordinate, documented range 0–1920 \|
	\| 3 \| Left Y coordinate, documented range 0–1080 \|
	\| 4 \| Right X coordinate, documented range 0–1920 \|
	\| 5 \| Right Y coordinate, documented range 0–1080 \|

	If both X and Y are zero, the gaze position is unknown. This usually means the observer's eyes were closed.

	## License files

	[`GazeData/LICENSE.txt`](GazeData/LICENSE.txt) specifies Creative Commons Attribution 4.0 International for gaze data and requests citation of the SAVAM paper.

	The original video notices are available here:

	- Source videos: [VQEG](VideoSources/VQEG_sources/LICENSE.txt), [LIVE](VideoSources/LIVE_sources/LICENSE.txt), [film and commercial excerpts](VideoSources/COPYRIGHTED_sources/LICENSE.txt).
	- Gaze-dot visualizations: [VQEG](DotsVisualisation/VQEG_sources_with_dots/LICENSE.txt), [LIVE](DotsVisualisation/LIVE_sources_with_dots/LICENSE.txt), [film and commercial excerpts](DotsVisualisation/COPYRIGHTED_sources_with_dots/LICENSE.txt).

	## Our related saliency datasets and papers

	\| Dataset \| Description \| Related paper \|
	\| --- \| --- \| --- \|
	\| [AudioVisualMouseSaliency (AViMoS)](https://huggingface.co/datasets/ANDRYHA/AudioVisualMouseSaliency) \| 1,500 Full HD videos with audio and crowdsourced mouse-tracking saliency annotations, used for the AIM 2024 challenge. \| Andrey Moskalenko et al. (2024). [AIM 2024 Challenge on Video Saliency Prediction: Methods and Results](https://arxiv.org/abs/2409.14827). \|
	\| [VideoSaliencyChallenge](https://huggingface.co/datasets/ANDRYHA/VideoSaliencyChallenge) \| 2,000 Full HD videos with audio and mouse-tracking saliency annotations, used for the NTIRE 2026 challenge. \| Andrey Moskalenko et al. (2026). [NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results](https://arxiv.org/abs/2604.14816). \|
	\| [OpenSAL360](https://huggingface.co/datasets/ANDRYHA/OpenSAL360) \| 500 omnidirectional videos with audio and crowdsourced saliency annotations from more than 2,000 observers. \| Alexey Bryncev et al. (2026). [OpenSAL360: Open-Source Crowdsourcing Platform for Omnidirectional Video Saliency Collection](https://arxiv.org/abs/2609.21480). \|

	## Citation

	```
	@INPROCEEDINGS {
	Gitm1410:Semiautomatic,
	AUTHOR = "Yury Gitman and Mikhail Erofeev and Dmitriy Vatolin
	and Andrey Bolshakov and Alexey Fedorov",
	TITLE = "Semiautomatic {Visual-Attention} Modeling and Its
	Application to Video Compression",
	BOOKTITLE = "2014 IEEE International Conference on Image Processing
	(ICIP) (ICIP 2014)",
	ADDRESS = "Paris, France",
	PAGES = "1105-1109",
	DAYS = 27,
	MONTH = oct,
	YEAR = 2014,
	KEYWORDS = "Saliency;Visual attention;Eye-tracking;Saliencyaware
	compression;H.264",
	}
	```

	[Accepted manuscript (PDF)](https://compression.ru/video/savam/pdf/Semiautomatic_visual_attention_modeling_and_its_application_to_video_compression.pdf) · [Published paper (IEEE)](https://ieeexplore.ieee.org/document/7025220)