Efficient Head Pose Estimation with Gabor Wavelet Networks

Krüger, Volker; Sommer, Gerald

doi:10.5244/c.14.8

Cited by 26 publications

(14 citation statements)

References 24 publications

(24 reference statements)

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“…Several authors have proposed using neural networks [28], [19], principal component analysis [8], and multidimensional Gaussian distributions [41] as modeling tools.…”

Section: Head-pose Trackingmentioning

confidence: 99%

Tracking the Visual Focus of Attention for a Varying Number of Wandering People

Smith

Odobez

et al. 2008

IEEE Trans. Pattern Anal. Mach. Intell.

108

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Abstract-In this paper, we define and address the problem of finding the visual focus of attention for a varying number of wandering people (VFOA-W), determining where a person is looking when their movement is unconstrained. The VFOA-W estimation is a new and important problem with implications in behavior understanding and cognitive science and real-world applications. One such application, presented in this paper, monitors the attention passers-by pay to an outdoor advertisement by using a single video camera. In our approach to the VFOA-W problem, we propose a multiperson tracking solution based on a dynamic Bayesian network that simultaneously infers the number of people in a scene, their body locations, their head locations, and their head pose. For efficient inference in the resulting variable-dimensional state-space, we propose a Reversible-Jump Markov Chain Monte Carlo (RJMCMC) sampling scheme and a novel global observation model, which determines the number of people in the scene and their locations. To determine if a person is looking at the advertisement or not, we propose Gaussian Mixture Model (GMM)-based and Hidden Markov Model (HMM)-based VFOA-W models, which use head pose and location information. Our models are evaluated for tracking performance and ability to recognize people looking at an outdoor advertisement, with results indicating good performance on sequences where up to three mobile observers pass in front of an advertisement.

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“…Several authors have proposed using neural networks [28], [19], principal component analysis [8], and multidimensional Gaussian distributions [41] as modeling tools.…”

Section: Head-pose Trackingmentioning

confidence: 99%

Tracking the Visual Focus of Attention for a Varying Number of Wandering People

Smith

Odobez

et al. 2008

IEEE Trans. Pattern Anal. Mach. Intell.

108

View full text Add to dashboard Cite

show abstract

“…Although the identity information can be well-suppressed, one main drawback of such techniques is that they are sensitive to the face alignment, background and scale. Some researchers also explored the problem by utilizing the geometric structure constrained by representative local features [15,16]. In [15], the authors extended the bunch graph work from [17] to pose estimation.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…Although this benefits the multi-view face recognition problem, it is not suitable for head pose estimation in a fine scale, since the elastic searching introduces ambiguity between similar poses. In [16], Gabor wavelets network, or GWN, which is constructed from the Gabor wavelets of local facial features, was used to estimate the head pose. One drawback is that it requires selected facial features to be visible, hence not suitable for head pose estimation with wide angle changes.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

An Integrated Two-Stage Framework for Robust Head Pose Estimation

Trivedi

2005

Lecture Notes in Computer Science

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Abstract. Subspace analysis has been widely used for head pose estimation. However, such techniques are usually sensitive to data alignment and background noise. In this paper a two-stage approach is proposed to address this issue by combining the subspace analysis together with the topography method. The first stage is based on the subspace analysis of Gabor wavelets responses. Different subspace techniques were compared for better exploring the underlying data structure. Nearest prototype matching using Euclidean distance was used to get the pose estimate. The single pose estimated was relaxed to a subset of poses around it to incorporate certain tolerance to data alignment and background noise. In the second stage, the uncertainty is eliminated by analyzing finer geometrical structure details captured by bunch graphs. This coarse-tofine framework was evaluated with a large data set. We examined 86 poses, with the pan angle spanning from −90 o to 90 o and the tilt angle spanning from −60 o to 45 o . The experimental results indicate that the integrated approach has a remarkably better performance than using subspace analysis alone.

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“…Since we are reporting results on classification methods, each method is limited by the pre-defined step size between classes. The method of [5] reports the highest accuracy; this result is achieved by using the same subject (a doll) for training as testing, a very small step size, and a unique representation based on a Gabor wavelet network. However, this method performs fine head tracking based on high-resolution data.…”

Section: Introductionmentioning

confidence: 99%

“…We refer to physical ground truth (GT) if an external physical sensor was applied such as the electromagnetic sensor used in [15] or the robotic arm used by [5]. Approximate ground truth (GT) refers to manual human annotation, which, not surprisingly, is not as accurate.…”

Section: Introductionmentioning

confidence: 99%