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Type: Conference paper
Title: High Five : recognising human interactions in TV shows
Author: Patron-Perez, A.
Marszalek, M.
Zisserman, A.
Reid, I.
Citation: Proceedings of the British Machine Vision Conference, 31 August-3rd September 2010 / Frédéric Labrosse, Reyer Zwiggelaar, Yonghuai Liu, Bernie Tiddeman (eds.): pp. 50.1-50.11
Publisher: BMVA Press
Publisher Place: United Kingdom
Issue Date: 2010
ISBN: 1901725405
Conference Name: British Machine Vision Conference (21st : 2010 : Aberystwyth, Wales, UK)
Statement of
Alonso Patron-Perez, Marcin Marszalek, Andrew Zisserman, Ian Reid
Abstract: In this paper we address the problem of recognising interactions between two people in realistic scenarios for video retrieval purposes. We develop a per-person descriptor that uses attention (head orientation) and the local spatial and temporal context in a neighbourhood of each detected person. Using head orientation mitigates camera view ambiguities, while the local context, comprised of histograms of gradients and motion, aims to capture cues such as hand and arm movement. We also employ structured learning to capture spatial relationships between interacting individuals. We train an initial set of one-vs-the-rest linear SVM classifiers, one for each interaction, using this descriptor. Noting that people generally face each other while interacting, we learn a structured SVM that combines head orientation and the relative location of people in a frame to improve upon the initial classification obtained with our descriptor. To test the efficacy of our method, we have created a new dataset of realistic human interactions comprised of clips extracted from TV shows, which represents a very difficult challenge. Our experiments show that using structured learning improves the retrieval results compared to using the interaction classifiers independently.
Rights: © 2010 The Authors
RMID: 0020131199
DOI: 10.5244/C.24.50
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Appears in Collections:Computer Science publications

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