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Human Action Recognition Using a Distributed RGB-Depth Camera Network
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DOI：10.1109/JSEN.2018.2859268.png)
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Abstract
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In this paper, we introduce the information-weighted consensus filter (IWCF)-based human pose estimation method for action recognition using a distributed RGBD camera network, which can handle the occlusion problem effectively existed in the single-view image. As far as we know, this is the first time that the novel consensus filter is proposed for distributed 3-D human pose estimation and action recognition. To demonstrate the performance of the proposed idea, we construct a small RGBD camera network and collect a number of actions from different persons. The final recognition results show that the IWCF-based human pose estimation method can converge to the centralized solution and achieve 93.75% accuracy rate after nine consensus iterations, which is much higher than the recognition results derived from single views.
Keywords:
Camera network
distributed information fusion
human action recognition
consensus filtering
Kinect
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