Improving robustness and precision in GEI + HOG action recognition

Tenika Whytock, Alexander Belyaev, Neil Robertson

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

4 Citations (Scopus)


Histograms of Oriented Gradients is a well known and applied descriptor, however “black box” use is common. Gradient computation is the key to performance and may be application dependent. In this paper we examine explicit, implicit and Hessian schemes as opposed to the recommended centred mask. Results indicate the explicit Bickley scheme boosts robustness, both static and dynamic information are important to recognition and full body Gait-Energy Images are preferred. Robustness is boosted by specific choice of cell and bin parameters and SVM where actions are pre-classified using temporal information.
Original languageEnglish
Title of host publicationAdvances in Visual Computing
Subtitle of host publication9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I
EditorsGeorge Bebis , Richard Boyle, Bahram Parvin, Darko Koracin, Baoxin Li, Fatih Porikli, Victor Zordan , James Klosowski , Sabine Coquillart, Xun Luo , Min Chen , David Gotz
Number of pages10
ISBN (Electronic)978-3-642-41914-0
ISBN (Print)978-3-642-41913-3
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science
ISSN (Electronic)0302-9743


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