Live streaming platforms need to store a lot of recorded live videos
\non a daily basis. An important problem is how to automatically
\nextract highlights (i.e., attractive short video clips) from these massive, long recorded live videos. One approach is to directly apply
\na highlight detection algorithm to video content. While various
\nalgorithms have been proposed, it is still hard to generalize them
\nwell to different types of videos without large training data or high
\ncomputing resources. In this paper, we propose to tackle this problem with a novel implicit crowdsourcing approach, called Lightor.
\nThe key insight is to collect users\u2019 natural interactions with a live
\nstreaming platform, and then leverage them to detect highlights.
\nLightor consists of two major components. Highlight Initializer
\ncollects time-stamped chat messages from a live video and then
\nuses them to predict approximate highlight positions. Highlight
\nExtractor keeps track of how users interact with these approximate
\nhighlight positions and then refines these positions iteratively. We
\nfind that the collected user chat and interaction data are very noisy,
\nand propose effective techniques to deal with noise. Lightor can
\nbe easily deployed into existing live streaming platforms, or be
\nimplemented as a web browser extension. We recruit hundreds of
\nusers from Amazon Mechanical Turk, and evaluate the performance
\nof Lightor on real live video data. The results show that Lightor
\ncan achieve high extraction precision with a small set of training
\ndata and low computing resources.<\/p>\n","protected":false},"excerpt":{"rendered":"
Live streaming platforms need to store a lot of recorded live videos on a daily basis. An important problem is how to automatically extract highlights (i.e., attractive short video clips) from these massive, long recorded live videos. One approach is to directly apply a highlight detection algorithm to video content. While various algorithms have been […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"IEEE International Conference on Data 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