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This paper proposes an object movement detection method covering large areas of a room by using multiple cameras. When object movement detection for whole of a room is performed, there are several challenging difficulties: sizes of objects on the camera images are small, non-objects such as humans also exist on the images, objects are sometimes difficult to detect in specific viewpoints because of...
Laser based environment recognition technologies have been developed recently. Especially moving objects detection and classification by laser scanners mounted on a mobility is required for mobile robots and autonomous cars. In this paper, we propose a moving objects detection and classification method based on grid trajectories acquired from sequential laser scan data. Grid trajectories are obtained...
In this paper, a novel approach is proposed to recover human body pose from 3D voxel data. The use of voxel data leads to viewpoint-free estimation, which benefits in that reconstruction of a training model is needless in different multi-camera arrangements. Other notable aspects of our approach are real-time ensuring speed (up to 30[FPS]), flexibility towards various complex motions, and robustness...
In this paper, we propose wrapped boosting that is extension of boosting algorithm for robust online action recognition. Boosting algorithm is one of ensemble learning algorithm and is also known as a feature selector. In our previous work utilizing boosting, we achieved automatic feature selection and robust model-based action classifiers which had very small calculation cost based on posture information...
In this paper, we propose a robust recognition and segmentation method for daily actions with a novel multi-task sequence labeling algorithm called multi-task conditional random field (MT-CRF). Multi-Task sequence labeling is a task of assigning input sequence to sequence of multi-labels that consist of one or multiple symbols in single frame. Multi-Task sequence labeling is essential for action recognition,...
In this paper, we propose a fast and robust online action recognition method. The main features of the proposed method are: 1) to select a small number of critical motion features from a very large set of motion feature templates and to release humans from task of designing critical motion features, 2) to require very small calculation cost for recognition compared to conventional methods, 3) to exploit...
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