RUSSIAN

SAVAM — Semiautomatic Visual-Attention Modeling

MSU Graphics & Media Lab (Video Group)

Projects, ideas: Dr. Dmitriy Vatolin, Prof. Galina Rozhkova
Implementation: Mikhail Erofeev, Yury Gitman, Andrey Bolshakov, Alexey Fedorov
In cooperation with IITP RAS

The database


Introduction


The maps of attention can be applied in many fields: user interface design, computer graphics, video processing, etc. Many technologies, algorithms and filters can be improved using information about the saliency distribution. During our work we have created the database of human eye-movements captured while viewing various videos (static and dynamic scenes, shots from cinema-like films and scientific databases)

Features/Benefits


High quality

  • Includes only FullHD and 4K UHDTV video sequences
  • Includes only stereoscopic video sequences
  • Eye-movements were captured with high quality eye-tracking device: SMI iViewXTM Hi-Speed 1250, with a 500 Hz frequency (20 fixation per frame)
  • Additional post-processing was applied to improve records' accuracy

Diversity

  • 43 fragments of motion video from various feature movies, commercial clips and stereo video databases
  • About 13 minutes of video (19760 frames)
  • 50 observers of different ages (mostly between 18–27 years old)
Please note: while the database contains S3D videos actually only the left view was demonstrated to observers.

Data post-processing


To improve data's accuracy several levels of verification and correction were applied.

The test sequence was divided into three five-minute parts. Before each part, we carried out the calibration procedure. The observer followed a target that was placed successively at 13 locations across the screen. Next, we validated the calibration by measuring the error of the gaze position at four points. If the estimated error was greater than 0.3 angular degrees, we restarted the calibration.

To reduce inter-video influence we inserted cross-fade by adding a black frame between adjacent scenes. Additionally, to measure observer fatigue we placed a special pattern after each three-scene part. We asked observers to track a stimulus, enabling us to measure the squared tracking error, which we defined as the fatigue value. On the next step, we improve the accuracy of determining the position of gaze using transformation, which is obtained by averaging of eye tracking data on calibrate pattern.

To understand the influence of an observer's fatigue on fixations at the end of a sequence, we asked eight observers to view the whole sequence a second time with the scenes appearing in reverse order.


Downloads


Download

Paper: Link to our paper (coming soon)
README: Link to README file with all info about the format of data (coming soon)
List of video sequences and some info: Link to file (coming soon)
List of observers and some info: Link to file (coming soon)
Source videos: Link to archive (coming soon)
Eye-tracking data before post-processing: Link to archive (coming soon)
Eye-tracking data after post-processing: Link to archive (coming soon)
Eye-movements visualizations (Circles): Link to archive (coming soon)
Eye-movements visualizations (Gaussians): Link to archive (coming soon)

Acknowledgments


This work was supported by the Intel/Cisco Video Aware Wireless Networking (VAWN) Program. We acknowledge Institute of Information Transmission Problems for help with eye tracking.