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aws-d:extended_video_database [2018/04/28 12:11] – [Extended Video Database - Information] victor.leboranaws-d:extended_video_database [2018/05/01 17:45] (actual) – [Extended Video Database - Information] victor.leboran
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 This eye tracking video database has been created to allow the comparison of Deep learning model against other saliency models. It can be used to validate visual attention models. This dataset includes 132 videos, we have selected a representative subset of videos using multiple known video datasets (CITIUS, CRCNS, DIEM, AE-UCFS, ASCMN, GC, Holly2Train, Holly2Test) acquired with different eye trackers. Most of them are too focused in a unique topic, so we have selected some representative videos for different categories. The videos can be classified in four categories, syntetic, urban, natural and indoors. The videos have been selected in order to minimize the influence of the top-down efects. This eye tracking video database has been created to allow the comparison of Deep learning model against other saliency models. It can be used to validate visual attention models. This dataset includes 132 videos, we have selected a representative subset of videos using multiple known video datasets (CITIUS, CRCNS, DIEM, AE-UCFS, ASCMN, GC, Holly2Train, Holly2Test) acquired with different eye trackers. Most of them are too focused in a unique topic, so we have selected some representative videos for different categories. The videos can be classified in four categories, syntetic, urban, natural and indoors. The videos have been selected in order to minimize the influence of the top-down efects.
  
 +===== Videos ======
 +  * //Number of videos// ... 132
 +  * //Video resolution// ..... 320×240 pixels
 +  * //Frame rate (fps)// ..... 30 fps (Synthetic) / 15 fps (other)
 +  * //Video format// .......... Mpeg 4
 +  * //Video duration// ........ Between 2 and 110 seconds.