Paper (ACM MM 2016) “Leveraging Contextual Cues for Generating Basketball Highlights”

October 18th, 2016 Irfan Essa Posted in ACM MM, Caroline Pantofaru, Computational Photography and Video, Computer Vision, Papers, Sports Visualization, Vinay Bettadapura No Comments »

Paper

  • V. Bettadapura, C. Pantofaru, and I. Essa (2016), “Leveraging Contextual Cues for Generating Basketball Highlights,” in Proceedings of ACM International Conference on Multimedia (ACM-MM), 2016. [PDF] [WEBSITE] [arXiv] [BIBTEX]
    @InProceedings{    2016-Bettadapura-LCCGBH,
      arxiv    = {http://arxiv.org/abs/1606.08955},
      author  = {Vinay Bettadapura and Caroline Pantofaru and Irfan
          Essa},
      booktitle  = {Proceedings of ACM International Conference on
          Multimedia (ACM-MM)},
      month    = {October},
      organization  = {ACM},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2016-Bettadapura-LCCGBH.pdf},
      title    = {Leveraging Contextual Cues for Generating
          Basketball Highlights},
      url    = {http://www.vbettadapura.com/highlights/basketball/index.htm},
      year    = {2016}
    }

Abstract

2016-Bettadapura-LCCGBH

Leveraging Contextual Cues for Generating Basketball Highlights

The massive growth of sports videos has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by broadcasters such as ESPN. Unlike previous works that mostly use audio-visual cues derived from the video, we propose an approach that additionally leverages contextual cues derived from the environment that the game is being played in. The contextual cues provide information about the excitement levels in the game, which can be ranked and selected to automatically produce high-quality basketball highlights. We introduce a new dataset of 25 NCAA games along with their play-by-play stats and the ground-truth excitement data for each basket. We explore the informativeness of five different cues derived from the video and from the environment through user studies. Our experiments show that for our study participants, the highlights produced by our system are comparable to the ones produced by ESPN for the same games.

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Paper in WACV (2015): “Egocentric Field-of-View Localization Using First-Person Point-of-View Devices”

January 6th, 2015 Irfan Essa Posted in Activity Recognition, Caroline Pantofaru, Computer Vision, First Person Computing, Mobile Computing, PAMI/ICCV/CVPR/ECCV, Papers, Vinay Bettadapura No Comments »

Paper

  • V. Bettadapura, I. Essa, and C. Pantofaru (2015), “Egocentric Field-of-View Localization Using First-Person Point-of-View Devices,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. (Best Paper Award) [PDF] [WEBSITE] [DOI] [arXiv] [BIBTEX]
    @InProceedings{    2015-Bettadapura-EFLUFPD,
      arxiv    = {http://arxiv.org/abs/1510.02073},
      author  = {Vinay Bettadapura and Irfan Essa and Caroline
          Pantofaru},
      awards  = {(Best Paper Award)},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.89},
      month    = {January},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Bettadapura-EFLUFPD.pdf},
      publisher  = {IEEE Computer Society},
      title    = {Egocentric Field-of-View Localization Using
          First-Person Point-of-View Devices},
      url    = {http://www.vbettadapura.com/egocentric/localization/},
      year    = {2015}
    }

Abstract

We present a technique that uses images, videos and sensor data taken from first-person point-of-view devices to perform egocentric field-of-view (FOV) localization. We define egocentric FOV localization as capturing the visual information from a person’s field-of-view in a given environment and transferring this information onto a reference corpus of images and videos of the same space, hence determining what a person is attending to. Our method matches images and video taken from the first-person perspective with the reference corpus and refines the results using the first-person’s head orientation information obtained using the device sensors. We demonstrate single and multi-user egocentric FOV localization in different indoor and outdoor environments with applications in augmented reality, event understanding and studying social interactions.

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