Publications

@inproceedings{2016-Bettadapura-LCCGBH,

title = {Leveraging Contextual Cues for Generating Basketball Highlights},

author = {Vinay Bettadapura and Caroline Pantofaru and Irfan Essa},

url = {https://dl.acm.org/doi/10.1145/2964284.2964286

http://www.vbettadapura.com/highlights/basketball/index.htm},

doi = {10.1145/2964284.2964286},

year  = {2016},

date = {2016-10-01},

urldate = {2016-10-01},

booktitle = {ACM International Conference on Multimedia (ACM-MM)},

organization = {ACM},

abstract = {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.},

keywords = {ACM, ACMMM, activity recognition, computational video, computer vision, sports visualization, video summarization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{2015-Thomaz-IMEARWSFASFS,

title = {Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study},

author = {Edison Thomaz and Cheng Zhang and Irfan Essa and Gregory Abowd},

url = {https://dl.acm.org/doi/10.1145/2678025.2701405},

doi = {10.1145/2678025.2701405},

year  = {2015},

date = {2015-05-01},

urldate = {2015-05-01},

booktitle = {ACM Conference on Intelligence User Interfaces (IUI)},

abstract = {Dietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors.},

keywords = {ACM, activity recognition, AI, awards, behavioral imaging, best paper award, computational health, IUI, machine learning},

pubstate = {published},

tppubtype = {inproceedings}

}