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metadata 
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

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.

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 of the official page.

Metadata

  • list_video.txt: video name, source sequence, and starting frame in that sequence.
  • 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 specifies Creative Commons Attribution 4.0 International for gaze data and requests citation of the SAVAM paper.

The original video notices are available here:

Our related saliency datasets and papers

Dataset Description Related paper
AudioVisualMouseSaliency (AViMoS) 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.
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.
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.

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) · Published paper (IEEE)