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Temporal video segmentation by event detection: A novelty detection approach

Authors:

Mahesh Venkata Krishna,

J. DenzlerAuthors Info & Claims

Pattern Recognition and Image Analysis, Volume 24, Issue 2

Pages 243 - 255

https://doi.org/10.1134/S1054661814020114

Published: 01 April 2014 Publication History

Abstract

Temporal segmentation of videos into meaningful image sequences containing some particular activities is an interesting problem in computer vision. We present a novel algorithm to achieve this semantic video segmentation. The segmentation task is accomplished through event detection in a frame-by-frame processing setup. We propose using one-class classification (OCC) techniques to detect events that indicate a new segment, since they have been proved to be successful in object classification and they allow for unsupervised event detection in a natural way. Various OCC schemes have been tested and compared, and additionally, an approach based on the temporal self-similarity maps (TSSMs) is also presented. The testing was done on a challenging publicly available thermal video dataset. The results are promising and show the suitability of our approaches for the task of temporal video segmentation.

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Cited By

Nixon LFoss JApostolidis KMezaris V(2022)Data-driven personalisation of television content: a surveyMultimedia Systems10.1007/s00530-022-00926-628:6(2193-2225)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s00530-022-00926-6
Chebiyyam MReddy RDogra DBhaskar HMihaylova L(2018)Motion anomaly detection and trajectory analysis in visual surveillanceMultimedia Tools and Applications10.1007/s11042-017-5196-677:13(16223-16248)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11042-017-5196-6
Dias CDimiccoli M(2018)Learning Event Representations by Encoding the Temporal ContextComputer Vision – ECCV 2018 Workshops10.1007/978-3-030-11015-4_44(587-596)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-11015-4_44
Show More Cited By

Temporal video segmentation by event detection: A novelty detection approach
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

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Information & Contributors

Information

Published In

cover image Pattern Recognition and Image Analysis

Pattern Recognition and Image Analysis Volume 24, Issue 2

April 2014

144 pages

ISSN:1054-6618

Issue’s Table of Contents

Copyright © Copyright © 2014 Pleiades Publishing, Ltd.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 April 2014

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Cited By

Nixon LFoss JApostolidis KMezaris V(2022)Data-driven personalisation of television content: a surveyMultimedia Systems10.1007/s00530-022-00926-628:6(2193-2225)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s00530-022-00926-6
Chebiyyam MReddy RDogra DBhaskar HMihaylova L(2018)Motion anomaly detection and trajectory analysis in visual surveillanceMultimedia Tools and Applications10.1007/s11042-017-5196-677:13(16223-16248)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11042-017-5196-6
Dias CDimiccoli M(2018)Learning Event Representations by Encoding the Temporal ContextComputer Vision – ECCV 2018 Workshops10.1007/978-3-030-11015-4_44(587-596)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-11015-4_44
Luo GLei GCao YLiu QSeo H(2017)Joint entropy-based motion segmentation for 3D animationsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-016-1313-133:10(1279-1289)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s00371-016-1313-1

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