NDLI: No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion

Content Provider	IEEE Xplore Digital Library
Author	Yan Yan Ricci, E. Subramanian, R. Lanz, O. Sebe, N.
Copyright Year	2013
Description	Author affiliation: Adv. Digital Sci. Center (ADSC), Univ. of Illinois at Urbana-Champaign, Singapore, Singapore (Subramanian, R.) \|\| Fondazione Bruno Kessler, Trento, Italy (Lanz, O.) \|\| Dept. of Inf. Eng. & Comput. Sci., Univ. of Trento, Trento, Italy (Yan Yan; Sebe, N.) \|\| Dept. of Electr. & Inf. Eng., Univ. of Perugia, Perugia, Italy (Ricci, E.)
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.
Sponsorship	IEEE Comput. Soc.
Starting Page	1177
Ending Page	1184
File Size	795549
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.150
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Head Cameras Magnetic heads Geometry Training Three-dimensional displays Computational modeling Head Pose Classification Multi-Task Learning Multi-view
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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