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User-Centric Environment Discovery With Camera Networks in Smart Homes
User-Centric Environment Discovery With Camera Networks in Smart Homes   (Citations: 2)
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We propose a data integration and reasoning technique for automatic environment discovery in smart homes based on observations of user interactions with objects. This approach is complementary to traditional appearance-based object recognition, which often demands large training sets. In our approach, object recognition is achieved in a semantic way by linking object behaviors to the pose and activity of the person using them. The complex relations between objects and user activities are modeled with a Markov logic network. The embodiments of the proposed approach in two multicamera smart environments are described, and the experimental results are presented. Index Terms—Activity analysis, camera networks, environment dis- covery, Markov logic network (MLN), object recognition, smart homes, statistical relational reasoning.
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    • ...In a related work, Wu and Aghajan [23] use the actions of the user as the context information to discover the objects in the environment...
    • ...Figure 2 some of the different views included in the Philips HomeLab dataset [23]...

    Rodrigo Cillaet al. Improving the Accuracy of Action Classification Using View-Dependent C...

    • ...Details of problem formulation based on employing a Markov Logic Network (MLN) to infer object types and experimental results can be found in [13, 14]...
    • ...Performance of activity analysis, object recognition, and comparison with an appearance-based object detection method can be found in [14]...

    Chen Wuet al. Use of Context in Video Processing

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