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We propose a novel approach to recognize action under view changes online with RGB-D camera. Perspective effects and camera motions have been considered as difficult problems in recognizing action that when two video sequences record a specific action from various camera views, the resulting appearances of actions would be entirely different. Consequently, if we simply apply feature extraction methods...
In this paper, we propose an efficient part-based approach for action recognition. The main concept is to recognize human actions by less occluded parts without using a large set of part filters. Therefore, our approach is robust to occlusion and cost-effective. We extract spatiotemporal features from RGB-D videos, and assign a part-label to each feature. Then, for each part, a recognition score is...
In this paper, we propose a Bayesian conditional probability with latent-structure model for context-aware activities of daily living (ADL) recognition. The proposed ADL recognition system takes RGBD sensor (Microsoft Kinect) as the input device. In ADL recognition, the object interacted with human is a sort of important context as well as human action. To better understand the activity, we model...
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