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Identifier 000424543
Title PerLNet: Learning to localize multiple periodic activities in real-world videos
Alternative Title PerLNet: Εντοπισμός πολλαπλών περιοδικών δραστηριοτήτων σε πραγματικά βίντεο με χρήση τεχνικών μάθησης
Author Καρβούνας, Γεώργιος Δ.
Thesis advisor Αργυρός, Αντώνης
Reviewer Τραχανιάς, Παναγιώτης
Ρούσσος, Αναστάσιος
Abstract This thesis addresses the problem of temporal periodicity localization, i.e., the problem of identifying all segments of a video that contain some sort of periodic or repetitive motion. To do so, the proposed method represents a video by the matrix of pairwise frame distances. These distances are computed on frame representations obtained with a convolutional neural network. On top of this representation, we design, implement and evaluate PerLNet, a convolutional neural network that is able to classify a given frame as belonging (or not) to a periodic video segment. An important characteristic of the employed representation is that it permits the handling of videos and periodic segments of arbitrary number and duration. Furthermore, the proposed training process requires a relatively small number of annotated videos. The proposed method drops several of the limiting assumptions of existing approaches regarding the contents of the video and the types of the observed periodic actions. Experimental results on recent, publicly available datasets validate our design choices, verify the generalization potential of PerLNet and demonstrate its superior performance in comparison to the current state of the art.
Language English
Subject Computer vision
Deap learning
Periodic activities
Περιοδικές δραστηριότητες
Υπολογιστική όραση
Issue date 2019-11-22
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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