Abstract book (pdf) - ICPR 2010

sidering the fact that the parts in an image are indeed the human body parts. In this paper, we present a method for 3D
human body modeling using range data that attempts to overcome these problems. In our approach the entire human body
is first decomposed into major body parts by a parts-based image segmentation method, and then a kinematics model is
fitted to the segmented body parts in an optimized manner. The fitted model is adjusted by the iterative closest point (ICP)
algorithm to resolve the gaps in the body data. Experimental results and comparisons demonstrate the effectiveness of our
approach.
09:00-11:10, Paper ThAT8.15
Scale Matching of 3D Point Clouds by Finding Keyscales with Spin Images
Tamaki, Toru, Hiroshima Univ.
Tanigawa, Shunsuke, Hiroshima Univ.
Ueno, Yuji, Hiroshima Univ.
Raytchev, Bisser, Hiroshima Univ.
Kaneda, Kazufumi, Hiroshima Univ.
In this paper we propose a method for matching the scales of 3D point clouds. 3D point sets of the same scene obtained
by 3D reconstruction techniques usually differ in scales. To match scales, we propose a keyscale that characterizes the
scale of a given 3D point cloud. By performing PCA of spin images over different scales, a keyscale is defined as the
scale that gives the minimum of cumulative contribution rate of PCA at a specific dimension of eigen space. Simulations
with the Stanford bunny and experimental results with 3D reconstructions of a real scene demonstrate that keyscales of
any 3D point clouds can be uniquely found and effectively used for scale matching.
09:00-11:10, Paper ThAT8.16
Tracking Multiple People with Illumination Maps
Zen, Gloria, Fondazione Bruno Kessler
Lanz, Oswald, Fondazione Bruno Kessler
Messelodi, Stefano, Fondazione Bruno Kessler
Ricci, Elisa, Fondazione Bruno Kessler
We address the problem of multiple people tracking under non-homogenous and time-varying illumination conditions.
We propose a unified framework for jointly estimating the position of the targets and their illumination conditions. For
each target multiple templates are considered to model appearance variations due to lighting changes. The template choice
is driven by an illumination map which describes the light conditions in different areas of the scene. This map is computed
with a novel algorithm for efficient inference in a hierarchical Markov Random Field (MRF) and is updated online to
adapt to slow lighting changes. Experimental results demonstrate the effectiveness of our approach.
09:00-11:10, Paper ThAT8.17
Combining Foreground / Background Feature Points and Anisotropic Mean Shift for Enhanced Visual Object
Tracking
Haner, Sebastian, Lund Univ. of Tech.
Gu, Irene Yu-Hua, Chalmers Univ. of Tech.
This paper proposes a novel visual object tracking scheme, exploiting both local point feature correspondences and global
object appearance using the anisotropic mean shift tracker. Using a RANSAC cost function incorporating the mean shift
motion estimate, motion smoothness and complexity terms, an optimal feature point set for motion estimation is found
even when a high proportion of outliers is presented. The tracker dynamically maintains sets of both foreground and background
features, the latter providing information on object occlusions. The mean shift motion estimate is further used to
guide the inclusion of new point features in the object model. Our experiments on videos containing long term partial occlusions,
object intersections and cluttered or close color distributed background have shown more stable and robust tracking
performance in comparison to three existing methods.
09:00-11:10, Paper ThAT8.18
Enhanced Measurement Model for Subspace-Based Tracking
Yin, Shimin, Seoul National Univ.
Yoo, Haan Ju, Seoul National Univ.
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