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Decoding of various motor intentions for generating command is one of the important factors in brain-based wheelchair system. The goal of this study focuses on classifying four types of trunk-related motor execution and imagery intentions. By brain components which are related to the trunk-related movements (waist, shoulder, and trunk) are generated in the very small and very close brain areas; therefore,...
In this paper, we address the problem of recognizing group activities that include interactions between human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene so as to be robust against noisy information. Two novel features,...
In this paper, we propose a new method to detect abnormal behavior in crowd video. The motion influence matrix is proposed to represent crowd behaviors. It is generated based on concept of human perception with block-level motion vectors which describe actual crowd movement. Furthermore, a generalized framework is developed to detect abnormal crowd behavior using motion influence matrix. The proposed...
Pose tracking technique has great potential for many applications such as marker-free human motion capture system, Human Computer Interactions (HCI), and video surveillance. Though many methods are introduced during last decades, self-occlusion - one body part is occluded by another one - is still considered one of the most difficult problems for 3D human pose tracking. In this paper, we propose a...
In human body pose estimation, manifold learning is a useful method for reducing the dimension of 2D images and 3D body configuration data. Most commonly, body pose is estimated from silhouettes derived from images or image sequences. A major problem when applying manifold estimation, however, is its vulnerability to silhouette variation. In this paper, we propose a novel approach to solving viewpoint-induced...
In this paper, we propose a new gesture recognition model for a set of both one-hand and two-hand gestures based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to the model. Unlike the coupled HMM, the proposed model has room for common hidden variables which are believed to be shared between two variables...
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