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  • Using Viewer's Facial Expression and Heart Rate for Sports Video Highlights Detection

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    Tjondronegoro224529-Accepted.pdf (770.5Kb)
    Author(s)
    Chakraborty, Prithwi Raj
    Tjondronegoro, Dian
    Zhang, Ligang
    Chandran, Vinod
    Griffith University Author(s)
    Tjondronegoro, Dian W.
    Year published
    2015
    Metadata
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    Abstract
    Viewer interest, evoked by video content, can potentially identify the highlights of the video. This paper explores the use of facial expressions (FE) and heart rate (HR) of viewers captured using camera and non-strapped sensor for identifying interesting video segments. The data from ten subjects with three videos showed that these signals are viewer dependent and not synchronized with the video contents. To address this issue, new algorithms are proposed to effectively combine FE and HR signals for identifying the time when viewer interest is potentially high. The results show that, compared with subjective annotation and ...
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    Viewer interest, evoked by video content, can potentially identify the highlights of the video. This paper explores the use of facial expressions (FE) and heart rate (HR) of viewers captured using camera and non-strapped sensor for identifying interesting video segments. The data from ten subjects with three videos showed that these signals are viewer dependent and not synchronized with the video contents. To address this issue, new algorithms are proposed to effectively combine FE and HR signals for identifying the time when viewer interest is potentially high. The results show that, compared with subjective annotation and match report highlights, 'non-neutral' FE and 'relatively higher and faster' HR is able to capture 60%-80% of goal, foul, and shot-ongoal soccer video events. FE is found to be more indicative than HR of viewer interest, but the fusion of these two modalities outperforms each of them.
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    Conference Title
    ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval
    DOI
    https://doi.org/10.1145/2671188.2749361
    Copyright Statement
    © ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, ISBN: 9781450332743, https://doi.org/10.1145/2671188.2749361
    Subject
    Artificial Intelligence and Image Processing
    Science & Technology
    Computer Science, Artificial Intelligence
    Computer Science, Information Systems
    Imaging Science & Photographic Technology
    Publication URI
    http://hdl.handle.net/10072/390266
    Collection
    • Conference outputs

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