Tracking with the Kinematics of Extremal Contours

Knossow, David; Ronfard, Rémi; Horaud, Radu; Devernay, Frédéric

doi:10.1007/11612032_67

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…Issues related to the minimization of the chamfer distance can be found in (Knossow et al 2006). Here we analyse the practical conditions under which this minimization should be carried out.…”

Section: By Noticing That D[x]mentioning

confidence: 99%

Human Motion Tracking with a Kinematic Parameterization of Extremal Contours

2007

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This paper addresses the problem of human motion tracking from multiple image sequences. The human body is described by five articulated mechanical chains and human body-parts are described by volumetric primitives with curved surfaces. If such a surface is observed with a camera, an extremal contour appears in the image whenever the surface turns smoothly away from the viewer. We describe a method that recovers human motion through a kinematic parameterization of these extremal contours. The method exploits the fact that the observed image motion of these contours is a function of both the rigid displacement of the surface and of the relative position and orientation between the viewer and the curved surface. First, we describe a parameterization of an extremal-contour point velocity for the case of developable surfaces. Second, we use the zeroreference kinematic representation and we derive an explicit formula that links extremal contour velocities to the angular velocities associated with the kinematic model. Third, we show how the chamfer-distance may be used to measure the discrepancy between predicted extremal contours and observed image contours; moreover we show how the chamfer distance can be used as a differentiable multi-valued function and how the tracker based on this distance can be cast into a continuous non-linear optimization framework. Fourth, we describe implementation issues associated with a practical human-body tracker that may use an arbitrary number of cameras. One great methodological and practical advantage of our method is that it relies neither on model-toimage, nor on image-to-image point matches. In practice we D. Knossow · R. Ronfard · R. Horaud ( ) INRIA Rhône-Alpes, 655, avenue de l'Europe, 38330 Montbonnot Saint-Martin, France e-mail: radu.horaud@inrialpes.fr model people with 5 kinematic chains, 19 volumetric primitives, and 54 degrees of freedom; We observe silhouettes in images gathered with several synchronized and calibrated cameras. The tracker has been successfully applied to several complex motions gathered at 30 frames/second.

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Section: By Noticing That D[x]mentioning

confidence: 99%

Human Motion Tracking with a Kinematic Parameterization of Extremal Contours

2007

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“…Note that a preliminary version of this work using an ad-hoc kinematic parametrization and a background substraction was described in [17]. In [17], we described a method using silhouettes and a standard edge detector (Canny-Deriche) without taking advantage of anisotropic filtering of color images.…”

Section: Introductionmentioning

confidence: 99%

“…In [17], we described a method using silhouettes and a standard edge detector (Canny-Deriche) without taking advantage of anisotropic filtering of color images. A main drawback of this work is its dependance on the silhouette estimation which often fails due to background substraction problems.…”

Section: Introductionmentioning

confidence: 99%

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Articulated-Body Tracking Through Anisotropic Edge Detection

et al. 2007

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Abstract. This paper addresses the problem of articulated motion tracking from image sequences. We describe a method that relies on both an explicit parameterization of the extremal contours and on the prediction of the human boundary edges in the image. We combine extremal contour prediction and edge detection in a non linear minimization process. The error function that measures the discrepancy between observed image edges and predicted model contours is minimized using an analytical expression of the Jacobian that maps joint velocities onto extremal contour velocities. In practice, we model people both by their geometry (truncated elliptic cones) and their articulated structure -a kinematic model with 40 rotational degrees of freedom. To overcome the flaws of standard edge detection, we introduce a model-based anisotropic Gaussian filter. The parameters of the anisotropic Gaussian are automatically derived from the kinematic model through the prediction of the extremal contours. The theory is validated by performing full body motion capture from six synchronized video sequences at 30 fps without markers.

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Conic Fitting Using the Geometric Distance

Sturm

Gargallo

Computer Vision – ACCV 2007

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International audienceWe consider the problem of fitting a conic to a set of 2D points. It is commonly agreed that minimizing geometrical error, i.e. the sum of squared distances between the points and the conic, is better than using an algebraic error measure. However, most existing methods rely on algebraic error measures. This is usually motivated by the fact that point-to-conic distances are difficult to compute and the belief that non-linear optimization of conics is computationally very expensive. In this paper, we describe a parameterization for the conic fitting problem that allows to circumvent the difficulty of computing point-to-conic distances, and we show how to perform the non-linear optimization process efficiently

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Tracking with the Kinematics of Extremal Contours

Cited by 4 publications

References 14 publications

Human Motion Tracking with a Kinematic Parameterization of Extremal Contours

Human Motion Tracking with a Kinematic Parameterization of Extremal Contours

Articulated-Body Tracking Through Anisotropic Edge Detection

Conic Fitting Using the Geometric Distance

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