Observing human-object interactions: using spatial and functional compatibility for recognition.

Full Abstract

Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene/event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape/appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information.

Author information

Author/s: Gupta, Abhinav (A); Kembhavi, Aniruddha (A); Davis, Larry S (LS);

Affiliation: University of Maryland, College Park, MD 20742, USA. agupta(-atsign-)cs.umd.edu

Journal and publication information

Publication Type: Journal Article

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2009-Oct; vol 31 (issue 10) : pp 1775-89

Dates: Created 2009/08/21; Completed 2009/10/06;

PMID: 19696449, status: MEDLINE (last retrieval date: 10/6/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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