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

Songlin Du, Takeshi Ikenaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
PublisherSpringer-Verlag
Pages539-549
Number of pages11
ISBN (Print)9783319773827
DOIs
Publication statusPublished - 2018 Jan 1
Event18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
Duration: 2017 Sep 282017 Sep 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10736 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

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

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Keywords

  • Local feature extraction
  • Object-aware keypoint selection
  • Spatio-temporal keypoint
  • Video analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Du, S., & Ikenaga, T. (2018). Local temporal coherence for object-aware keypoint selection in video sequences. In Advances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers (pp. 539-549). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10736 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-77383-4_53