Multi-view Based Estimation of Human Upper-Body Orientation

Rybok, Lukas; Voit, Michael; Ekenel, Hazım Kemal; Stiefelhagen, Rainer

doi:10.1109/icpr.2010.385

Cited by 14 publications

(6 citation statements)

References 10 publications

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“…Our results are difficult to compare with those reported for techniques based on a classification method, such as the one proposed by (Rybok et al, 2010), instead of a regression mechanism. Therefore, we limit our comparison to results expressed in terms of an error angle.…”

Section: First Experiment: Choosing a Shape Descriptorcontrasting

confidence: 53%

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Estimation of Human Orientation Based on Silhouettes and Machine Learning Principles

Pierard

Droogenbroeck

2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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Abstract:Estimating the orientation of the observed person is a crucial task for home entertainment, man-machine interaction, intelligent vehicles, etc. This is possible but complex with a single camera because it only provides one side view. To decrease the sensitivity to color and texture, we use the silhouette to infer the orientation.Under these conditions, we show that the only intrinsic limitation is to confuse the orientation θ with the supplementary angle (that is 180°− θ), and that the shape descriptor must distinguish between mirrored images. In this paper, the orientation estimation is expressed and solved in the terms of a regression problem and supervised learning. In our experiments, we have tested and compared 18 shape descriptors; the best one achieves a mean error of 5.24°. However, because of the intrinsic limitation mentioned above, the range of orientations is limited to 180°. Our method is easy to implement and outperforms existing techniques.

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Section: First Experiment: Choosing a Shape Descriptorcontrasting

confidence: 53%

“…Then, a nonlinear least square technique provides an estimate of the orientation. Rybok et al (Rybok et al, 2010) also demonstrate that using several silhouettes leads to better results. They use shape contexts to describe each silhouette separately and combine the single view results within a Bayesian filter framework.…”

Section: Related Workmentioning

confidence: 93%

Estimation of Human Orientation Based on Silhouettes and Machine Learning Principles

Pierard

Droogenbroeck

2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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“…So the role played by 3D in orientation estimation is much more important than the one played by perspective effects. Moreover, these results demonstrate that several viewpoints (as used in [8] and [14]) are useless for the orientation estimation. …”

Section: The Role Played By 3d In Orientation Estimationmentioning

confidence: 85%

“…Rybok et al [14] establish that the use of several silhouettes leads to better results. They consider shape contexts to describe each silhouette separately and combine individual results with a Bayesian filter framework.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Human Orientation in Images Captured with a Range Camera

Pierard

Leroy

Hansen

et al. 2011

Advanced Concepts for Intelligent Vision Systems

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Estimating the orientation of the observed person is a crucial task for some application fields like home entertainment, man-machine interaction, or intelligent vehicles. In this paper, we discuss the usefulness of conventional cameras for estimating the orientation, present some limitations, and show that 3D information improves the estimation performance. Technically, the orientation estimation is solved in the terms of a regression problem and supervised learning. This approach, combined to a slicing method of the 3D volume, provides mean errors as low as 9.2°o r 4.3°depending on the set of considered poses. These results are consistent with those reported in the literature. However, our technique is faster and easier to implement than existing ones.

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“…Using a time frame in units of frames, a lot of research is done in the field of gaze and head-pose estimation [53,84,91,92,99,105].…”

Section: Hba Taxonomiesmentioning

confidence: 99%

A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

Chaaraoui

Climent-Pérez

Flórez-Revuelta

2012

Expert Systems with Applications

182

122

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Human Behaviour Analysis (HBA) is more and more being of interest for Computer Vision and Artificial Intelligence researchers. Its main application areas, like Video Surveillance and Ambient-Assisted Living (AAL), have been in great demand in recent years. This paper provides a review on HBA for AAL and ageing in place purposes focusing specially on vision techniques. First, a clearly defined taxonomy is presented in order to classify the reviewed works, which are consequently presented following a bottom-up abstraction and complexity order. At the motion level, pose and gaze estimation as well as basic human movement recognition are covered. Next, the mainly used action and activity recognition approaches are presented with examples of recent research works. Increasing the degree of semantics and the time interval involved in the HBA, finally the behaviour level is reached. Furthermore, useful tools and datasets are analysed in order to provide help for initiating projects.

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Multi-view Based Estimation of Human Upper-Body Orientation

Cited by 14 publications

References 10 publications

Estimation of Human Orientation Based on Silhouettes and Machine Learning Principles

Estimation of Human Orientation Based on Silhouettes and Machine Learning Principles

Estimation of Human Orientation in Images Captured with a Range Camera

A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

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