Clothed people detection in still images

Sprague, Nathan; Luo, Jiebo

doi:10.1109/icpr.2002.1048007

Cited by 20 publications

(19 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Despite this, few authors have looked at using clothing in the process [12] or at actually using a model of the clothing. Recent work by Bourdev and Malik [13] learns body part detectors that include upper and lower clothing regions.…”

Section: Related Workmentioning

confidence: 99%

A 2D Human Body Model Dressed in Eigen Clothing

Guan¹,

Freifeld

Black³

2010

Computer Vision – ECCV 2010

View full text Add to dashboard Cite

Abstract. Detection, tracking, segmentation and pose estimation of people in monocular images are widely studied. Two-dimensional models of the human body are extensively used, however, they are typically fairly crude, representing the body either as a rough outline or in terms of articulated geometric primitives. We describe a new 2D model of the human body contour that combines an underlying naked body with a lowdimensional clothing model. The naked body is represented as a Contour Person that can take on a wide variety of poses and body shapes. Clothing is represented as a deformation from the underlying body contour. This deformation is learned from training examples using principal component analysis to produce eigen clothing. We find that the statistics of clothing deformations are skewed and we model the a priori probability of these deformations using a Beta distribution. The resulting generative model captures realistic human forms in monocular images and is used to infer 2D body shape and pose under clothing. We also use the coefficients of the eigen clothing to recognize different categories of clothing on dressed people. The method is evaluated quantitatively on synthetic and real images and achieves better accuracy than previous methods for estimating body shape under clothing.

show abstract

Section: Related Workmentioning

confidence: 99%

A 2D Human Body Model Dressed in Eigen Clothing

Guan¹,

Freifeld

Black³

2010

Computer Vision – ECCV 2010

View full text Add to dashboard Cite

show abstract

“…For instance, [4] applies color segmentation for extracting uniform regions from the analyzed images. These regions are then matched to the nodes of a tree that represents the constituent parts of a person, as well as their associated topological relationships and probability distributions.…”

Section: Previous Related Workmentioning

confidence: 99%

Robust People Detection by Fusion of Evidence from Multiple Methods

Fernández-Carbajales

García

Martinez

2008

2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services

View full text Add to dashboard Cite

show abstract

“…Model based methods use the structure of the human body to detect people in images [14], [16]. These approaches find parts of the human body, such as the head, hands, feet, and legs in a scene and construct a relationship between each component.…”

Section: Related Workmentioning

confidence: 99%

Toward real-time human detection and tracking in diverse environments

Koenig

2007

2007 IEEE 6th International Conference on Development and Learning

View full text Add to dashboard Cite

Abstract-Human-robot interaction (HRI) encompasses numerous disciplines from psychology to mechanical engineering. Each field contributes to a robot's ability to better understand and respond to humans actions, and behaviors. A common thread is the need to detect a person and their location within an environment. In this paper we tackle the issue of identifying which objects in a scene are people. We also discuss current work on human detection, and strategies for improved performance.

show abstract

Clothed people detection in still images

Cited by 20 publications

References 4 publications

A 2D Human Body Model Dressed in Eigen Clothing

A 2D Human Body Model Dressed in Eigen Clothing

Robust People Detection by Fusion of Evidence from Multiple Methods

Toward real-time human detection and tracking in diverse environments

Contact Info

Product

Resources

About