VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: More About Stimuli
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This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: [email protected]);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: [email protected]);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: [email protected]);
(4) Zhimin Chen, University of California, Berkeley (Email: [email protected]);
(5) Yunhui Guo, University of Texas at Dallas (Email: [email protected]);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: [email protected]);
(7) David Whitney, University of California, Berkeley (Email: [email protected]).
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Table of Links
Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References
7. More About Stimuli
All videos used in the VEATIC dataset were selected from an online video-sharing website (YouTube). The VEATIC dataset contains 124 video clips, 104 clips from Hollywood movies, 15 clips from home videos, and 5 clips from documentaries or reality TV shows. Specifically, we classify Documentary videos as any videos that show candid social interactions but have some form of video editing, while home videos refer to videos that show candid social interactions without any video editing. All Videos in the dataset had a frame rate of 25 frames per second and ranged in resolution with the lowest being 202 x 360 and the highest being 1920 x 1080.
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Except for the overview of video frames in Figure 2, we show more samples in Figure 9. Moreover, unlike previously published datasets where most frames contain the main character [31, 29, 32], VEATIC not only has frames containing the selected character but also there are lots of frames containing unselected characters and pure backgrounds (Figure 10). Therefore, VEATIC is more similar to our daily life scenarios, and the algorithms trained on it will be more promising for daily applications.
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This paper is available on arxiv under CC 4.0 license.
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