Leveraging textural features for recognizing actions in low quality videos

Saimunur Rahman, John See, Chiung Ching Ho

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

Abstract

Human action recognition is a well researched problem, which is considerably more challenging when video quality is poor. In this paper, we investigate human action recognition in low quality videos by leveraging the robustness of textural features to better characterize actions, instead of relying on shape and motion features may fail under noisy conditions. To accommodate videos, texture descriptors are extended to three orthogonal planes (TOP) to extract spatio-temporal features. Extensive experiments were conducted on lowquality versions of theKTH and HMDB51 datasets to evaluate the performance of our proposed approaches against standard baselines. Experimental results and further analysis demonstrated the usefulness of textural features in improving the capability of recognizing human actions from low quality videos.

Original languageEnglish
Title of host publication9th International Conference on Robotic, Vision, Signal Processing and Power Applications
EditorsHaidi Ibrahim, Shahid Iqbal, Soo Siang Teoh, Mohd Tafir Mustaffa
PublisherSpringer
Pages237-245
Number of pages9
ISBN (Electronic)9789811017216
ISBN (Print)9789811017193
DOIs
Publication statusPublished - 30 Sep 2016
Event9th International Conference on Robotic, Vision, Signal Processing and Power Applications 2016 - , Malaysia
Duration: 2 Feb 20163 Feb 2016

Publication series

NameLecture Notes in Electrical Engineering
Volume398
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference9th International Conference on Robotic, Vision, Signal Processing and Power Applications 2016
Abbreviated titleRoViSP 2016
Country/TerritoryMalaysia
Period2/02/163/02/16

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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