Multi-View Semantic Temporal Video Segmentation

Thomas Theodoridis, Anastasios Tefas, Ioannis Pitas

Research output: Contribution to conferenceConference Paperpeer-review

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In this work, we propose a multi-view temporal video segmentation approach that employs a Gaussian scoring process for determining the best segmentation positions. By exploiting the semantic action information that the dense trajectories video description offers, this method can detect intra-shot actions
as well, unlike shot boundary detection approaches. We compare the temporal segmentation results of the proposed method to both single-view and multi-view methods, and also compare the action recognition results obtained on ground truth video segments to the ones obtained on the proposed multi-view segments, on the IMPART multi-view action data set.
Original languageEnglish
Publication statusPublished - 25 Sept 2016
EventIEEE International Conference on Image Processing - Phoenix, Arizona, United States
Duration: 25 Sept 201628 Sept 2016


ConferenceIEEE International Conference on Image Processing
Abbreviated titleICIP
Country/TerritoryUnited States
CityPhoenix, Arizona


  • temporal video segmentation
  • action recognition
  • IMPART multi-view action data set


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