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Interactive event detection in crowd scenes

Published: 09 September 2012 Publication History

Abstract

As an important aspect in video content analysis, event detection is still an open problem. In particular, the study on detecting interactive events in crowd scenes is still limited. In this paper, we investigate detecting interactive events between persons, e.g. PeopleMeet, PeopleSplitUp and Embrace in complex scenes using a sequence learning based approach. By sequence learning, the spatial-temporal context information is introduced in the learning stage. Experiments have been performed over TRECVid Event Detection 2010 dataset, which contains totally 144 hours surveillance video of London Gatwick airport. According to the TRECVid-ED 2010 formal evaluation, our approach obtains promising results, with the top performance (NDCR) for PeopleMeet and PeopleSplit-Up, and second-best performance (NDCR) for Embrace.

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cover image ACM Other conferences
ICIMCS '12: Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
September 2012
243 pages
ISBN:9781450316002
DOI:10.1145/2382336
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • National Science Foundation of China
  • CCNU: Central China Normal University
  • Daqian Vision: Daqian Vision
  • Microsoft Research: Microsoft Research
  • Beijing ACM SIGMM Chapter
  • NEC: NEC Labs China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2012

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Author Tags

  1. TRECVid
  2. events detection
  3. sequence learning

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  • Research-article

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ICIMCS '12
Sponsor:
  • CCNU
  • Daqian Vision
  • Microsoft Research
  • NEC

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Overall Acceptance Rate 163 of 456 submissions, 36%

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