Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT8.28
Incremental MPCA for Color Object Tracking
Wang, Dong, Department of Electronic Engineering
Lu, Hu-Chuan, Dalian Univ. of Tech.
Chen, Yen-Wei, Ritsumeikan Univ.
The task of visual tracking is to deal with dynamic image streams that change over time. For color object tracking, although
a color object is a 3-order tensor in essence, little attention has been focused on this attribute. In this paper, we propose a
novel Incremental Multiple Principal Component Analysis (IMPCA) method for online learning dynamic tensor streams.
When newly added tensor set arrives, the mean tenor and the covariance matrices of different modes can be updated easily,
and then projection matrices can be effectively calculated based on covariance matrices. Finally, we apply our IMPCA method
to color object tracking using Bayes inference framework. Experiments are performed on some changeling public and our
own video sequences. The experimental results demonstrate that the proposed method achieves considerable performance.
13:30-16:30, Paper TuBCT8.29
Epipolar-Based Stereo Tracking without Explicit 3D Reconstruction
Gaschler, Andre Karlheinz, Tech. Univ. München
Burschka, Darius, Tech. Univ. München
Hager, Gregory
We present a general framework for tracking image regions in two views simultaneously based on sum-of-squared differences
(SSD) minimization. Our method allows for motion models up to affine transformations. Contrary to earlier approaches, we
incorporate the well-known epipolar constraints directly into the SSD optimization process. Since the epipolar geometry can
be computed from the image directly, no prior calibration is necessary. Our algorithm has been tested in different applications
including camera localization, wide-baseline stereo, object tracking and medical imaging. We show experimental results on
robustness and accuracy compared to the known ground truth given by a conventional tracking device.
13:30-16:30, Paper TuBCT8.30
Human Body Parts Tracking using Sequential Markov Random Fields
Cao, Xiao-Qin, City Univ. of Hong Kong
Zeng, Jia, Soochow University
Liu, Zhi-Qiang, City Univ. of Hong Kong
Automatically tracking human body parts is a difficult problem because of background clutters, missing body parts, and the
high degrees of freedoms and complex kinematics of the articulated human body. This paper presents the sequential Markov
random fields (SMRFs) for tracking and labeling moving human body parts automatically by learning the spatio-temporal
structures of human motions in the setting of occlusions and clutters. We employ a hybrid strategy, where the temporal dependencies
between two successive human poses are described by the sequential Monte Carlo method, and the spatial relationships
between body parts in a pose is described by the Markov random fields. Efficient inference and learning algorithms
are developed based on the relaxation labeling. Experimental results show that the SMRF can effectively track human body
parts in natural scenes.
13:30-16:30, Paper TuBCT8.31
Action Recognition in Videos using Nonnegative Tensor Factorization
Krausz, Barbara, Fraunhofer IAIS
Bauckhage, Christian, Fraunhofer IAIS
Recognizing human actions is of vital interest in video surveillance or ambient assisted living. We consider an action as a
sequence of body poses which are themselves a linear combination of body parts. In an offline procedure, nonnegative tensor
factorization is used to extract basis images that represent body parts. The weighting coefficients are obtained by filtering a
frame with the set of basis images. Since the basis images are obtained from nonnegative tensor factorization, they are separable
and filtering can be implemented efficiently. The weighting coefficients encode dynamics and are used for action
recognition. In the proposed action recognition framework, neither explicit detection and tracking of humans nor background
subtraction are needed. Furthermore, for recognizing location specific actions, we implicitely take scene objects into account.
- 136 -