No matter where you are: Flexible graph-guided multi-task learning for multi-view head pose classification under target motion

Yan Yan, Elisa Ricci, Ramanathan Subramanian, Oswald Lanz, Nicu Sebe

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

101 Citations (Scopus)

Abstract

We propose a novel Multi-Task Learning framework (FEGA-MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. As the target (person) moves, distortions in facial appearance owing to camera perspective and scale severely impede performance of traditional head pose classification methods. FEGA-MTL operates on a dense uniform spatial grid and learns appearance relationships across partitions as well as partition-specific appearance variations for a given head pose to build region-specific classifiers. Guided by two graphs which a-priori model appearance similarity among (i) grid partitions based on camera geometry and (ii) head pose classes, the learner efficiently clusters appearance wise related grid partitions to derive the optimal partitioning. For pose classification, upon determining the target's position using a person tracker, the appropriate region specific classifier is invoked. Experiments confirm that FEGA-MTL achieves state-of-the-art classification with few training data.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
EditorsKyros Kutulakos, Yi Ma, Steve Seitz, Phil Torr
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1177-1184
Number of pages8
ISBN (Print)9781479928392
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
Duration: 1 Dec 20138 Dec 2013

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference2013 14th IEEE International Conference on Computer Vision, ICCV 2013
Country/TerritoryAustralia
CitySydney, NSW
Period1/12/138/12/13

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