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Authors: Ankit Sharma 1 ; Apurv Kumar 1 ; Sony Allappa 1 ; Veena Thenkanidiyoor 1 ; Dileep Aroor Dinesh 2 and Shikha Gupta 2

Affiliations: 1 National Institute of Technology Goa, India ; 2 Indian Institute of Technology Mandi, India

ISBN: 978-989-758-276-9

Keyword(s): Video Activity Recognition, Gaussian Mixture Model based Encoding, Support Vector Machine, Histogram Intersection Kernel, Hellinger Kernel, Time Flexible Kernel, Modified Time Flexible Kernel.

Related Ontology Subjects/Areas/Topics: Applications ; Classification ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Kernel Methods ; Pattern Recognition ; Software Engineering ; Theory and Methods ; Video Analysis

Abstract: Video activity recognition involves automatically assigning a activity label to a video. This is a challenging task due to the complex nature of video data. There exists many sub activities whose temporal order is important. For building an SVM-based activity recognizer it is necessary to use a suitable kernel that considers varying length temporal data corresponding to videos. In (Mario Rodriguez and Makris, 2016), a time flexible kernel (TFK) is proposed for matching a pair of videos by encoding a video into a sequence of bag of visual words (BOVW) vectors. The TFK involves matching every pair of BOVW vectors from a pair of videos using linear kernel. In this paper we propose modified TFK (MTFK) where better approaches to match a pair of BOVW vectors are explored. We propose to explore the use of frequency based kernels for matching a pair of BOVW vectors. We also propose an approach for encoding the videos using Gaussian mixture models based soft clustering technique. The effective ness of the proposed approaches are studied using benchmark datasets. (More)

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Paper citation in several formats:
Sharma, A.; Kumar, A.; Allappa, S.; Thenkanidiyoor, V.; Aroor Dinesh, D. and Gupta, S. (2018). Modified Time Flexible Kernel for Video Activity Recognition using Support Vector Machines.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 133-140. DOI: 10.5220/0006595501330140

@conference{icpram18,
author={Ankit Sharma. and Apurv Kumar. and Sony Allappa. and Veena Thenkanidiyoor. and Dileep Aroor Dinesh. and Shikha Gupta.},
title={Modified Time Flexible Kernel for Video Activity Recognition using Support Vector Machines},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={133-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006595501330140},
isbn={978-989-758-276-9},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Modified Time Flexible Kernel for Video Activity Recognition using Support Vector Machines
SN - 978-989-758-276-9
AU - Sharma, A.
AU - Kumar, A.
AU - Allappa, S.
AU - Thenkanidiyoor, V.
AU - Aroor Dinesh, D.
AU - Gupta, S.
PY - 2018
SP - 133
EP - 140
DO - 10.5220/0006595501330140

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