Abstract book (pdf) - ICPR 2010

letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating
the right hand, down by activating the left toes and up by activating the right toes. To select a letter, the subject
simply rests for a number of seconds. We can thus communicate with the subject in the scanner by for example showing
questions that the subject can answer. Similar BCI for communication have been made with electroencephalography
(EEG). In these implementations the subject for example focuses on a letter while different rows and columns of the virtual
keyboard are flashing. The system then tries to detect if the correct letter is flashing or not. In our setup we instead classify
the brain activity. Our system is not limited to a communication interface, but can be used for any interface where five degrees
of freedom is necessary.
09:00-11:10, Paper ThAT9.4
Combined Top-Down/Bottom-Up Human Articulated Pose Estimation using AdaBoost Learning
Wang, Sheng, Tsinghua Univ.
Ai, Haizhou, Tsinghua Univ.
Yamashita, Takayoshi, OMRON Corp.
Lao, Shihong, OMRON Corp.
In this paper, a novel human articulated pose estimation method based on AdaBoost algorithm is presented. The human
articulated pose is estimated by locating major human joint positions. We learn the classifiers on a normalized image for
classifying each pixel position into a certain category. Two different kinds of classifiers, bottom-up joint position classifier
and top-down skeleton classifier, are combined to achieve final results. HOG (Histogram of Oriented Gradient) feature is
used for training both classifiers. Our human pose estimation system consists of three models, human detection, view classification,
and pose estimation. The implemented system can automatically estimate human pose of different views. Experiment
results are reported to show our proposed method can work on relatively small-size human images without using
human silhouettes as a prerequisite, which is very efficient, robust and accurate enough for potential applications in visual
surveillance.
09:00-11:10, Paper ThAT9.5
The Human Action Image
Sethi, Ricky, Univ. of California, Riverside
Roy-Chowdhury, Amit, Univ. of California, Riverside
Recognizing a person’s motion is intuitive for humans but represents a challenging problem in machine vision. In this
paper, we present a multi-disciplinary framework for recognizing human actions. We develop a novel descriptor, the
Human Action Image (HAI): a physically-significant, compact representation for the motion of a person, which we derive
from first principles in physics using Hamilton’s Action. We embed the HAI as the Motion Energy Pathway of the latest
Neurobiological model of motion recognition. The Form Pathway is modelled using existing low-level feature descriptors
based on shape and appearance. Experimental validation of the theory is provided on the well-known Weizmann and USF
Gait datasets.
09:00-11:10, Paper ThAT9.6
Combining Spatial and Temporal Information for Gait based Gender Classification
Hu, Maodi, Beihang Univ.
Wang, Yunhong, Beihang Univ.
Zhang, Zhaoxiang, Beihang Univ.
Wang, Yiding, North China Univ. of Tech.
In this paper, we address the problem of gait based gender classification. The Gabor feature which is a new attempt for
gait analysis, not only improves the robustness to the segmental noise, but also provides a feasible way to purge the additional
influence factors like clothing and carrying condition changes before supervised learning. Furthermore, through the
agency of Maximization of Mutual Information (MMI), the low dimensional discriminative representation is obtained as
the Gabor-MMI feature. After that, gender related Gaussian Mixture Model-Hidden Markov Models (GMM-HMMs) are
constructed for classification work. In this case, supervised learning reduces the dimension of parameter space, and significantly
increases the gap between likelihoods of the gender models. In order to assess the performance of our proposed
approach, we compare it with other methods on the standard CASIA Gait Databases (Dataset B). Experimental results
demonstrate that our approach achieves better Correct Classification Rate (CCR) than the state of the art methods.
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