Action Recognition for Videos by Long-Term Point Trajectory Analysis with Background Removal

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

Recently, dense trajectories were shown to be an efficient video motion representation for action recognition and achieved state-of-the-art results on a variety of video datasets. This paper improves their performance by taking into account camera motion. To estimate camera motion, the authors use long-term point trajectory analysis to cluster image points and propose an algorithm to find possible background cluster from these clusters according to background nature in a video. Considering the original clusters could not segment the foreground and background very well. The authors optimize the background cluster, and use the cluster to rectify the trajectory. Experimental results on three challenging action datasets (i.e., Hollywood2, Olympic Sports and UCF50) show that the rectified trajectories significantly outperform original dense trajectories.

本文言語英語
ホスト出版物のタイトルProceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016
編集者Giuseppe De Pietro, Albert Dipanda, Richard Chbeir, Luigi Gallo, Kokou Yetongnon
出版社Institute of Electrical and Electronics Engineers Inc.
ページ23-30
ページ数8
ISBN(電子版)9781509056989
DOI
出版ステータス出版済み - 4 21 2017
イベント12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016 - Naples, イタリア
継続期間: 11 28 201612 1 2016

出版物シリーズ

名前Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016

その他

その他12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016
Countryイタリア
CityNaples
Period11/28/1612/1/16

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Radiology Nuclear Medicine and imaging
  • Computer Networks and Communications
  • Signal Processing

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