Local temporal coherence for object-aware keypoint selection in video sequences

Songlin Du, Takeshi Ikenaga

研究成果: Conference contribution

抄録

Local feature extraction is an important solution for video analysis. The common framework of local feature extraction consists of a local keypoint detector and a keypoint descriptor. Existing keypoint detectors mainly focus on the spatial relationships among pixels, resulting in a large amount of redundant keypoints on background which are often temporally stationary. This paper proposes an object-aware local keypoint selection approach to keep the active keypoints on object and to reduce the redundant keypoints on background by exploring the temporal coherence among successive frames in video. The proposed approach is made up of three local temporal coherence criteria: (1) local temporal intensity coherence; (2) local temporal motion coherence; (3) local temporal orientation coherence. Experimental results on two publicly available datasets show that the proposed approach reduces more than 60% keypoints, which are redundant, and doubles the precision of keypoints.

本文言語English
ホスト出版物のタイトルAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
出版社Springer-Verlag
ページ539-549
ページ数11
ISBN(印刷版)9783319773827
DOI
出版ステータスPublished - 2018 1 1
イベント18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
継続期間: 2017 9 282017 9 29

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10736 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other18th Pacific-Rim Conference on Multimedia, PCM 2017
CountryChina
CityHarbin
Period17/9/2817/9/29

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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