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首页> 外文期刊>IEEE Transactions on Emerging Topics in Computational Intelligence >Behavior Recognition Using Multiple Depth Cameras Based on a Time-Variant Skeleton Vector Projection
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Behavior Recognition Using Multiple Depth Cameras Based on a Time-Variant Skeleton Vector Projection

机译：基于时变骨架矢量投影的多深度相机行为识别

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摘要

User behavior recognition in a smart office environment is a challenging research task. Wearable sensors can be used to recognize behaviors, but such sensors could go unworn, making the recognition task unreliable. Cameras are also used to recognize behaviors, but occlusions and unstable lighting conditions reduce such methods' recognition accuracy. To address these problems, we propose a time-variant skeleton vector projection scheme using multiple infrared-based depth cameras for behavior recognition. The contribution of this paper is threefold: (1) The proposed method can extract reliable projected skeleton vector features by compensating occluded data using nonoccluded data; (2) the proposed occlusion-based weighting element generation can be employed to train support-vector-machine-based classifiers to recognize behaviors in a multiple-view environment; and (3) the proposed method achieves superior behavior recognition accuracy and involves less computational complexity compared with other state-of-the-art methods for practical testing environments.
机译：智能办公环境中的用户行为识别是一项具有挑战性的研究任务。可穿戴式传感器可用于识别行为，但此类传感器可能会磨损，从而使识别任务变得不可靠。相机也用于识别行为，但是遮挡和不稳定的光照条件会降低此类方法的识别精度。为了解决这些问题，我们提出了一种时变骨架矢量投影方案，该方案使用多个基于红外的深度相机进行行为识别。本文的贡献有三点：（1）提出的方法可以通过使用非遮挡数据补偿遮挡数据来提取可靠的投影骨架矢量特征。 （2）提出的基于遮挡的加权元素生成可用于训练基于支持向量机的分类器，以识别多视图环境中的行为； （3）与实际测试环境中的其他最新方法相比，该方法具有较高的行为识别精度，并且计算复杂度较低。

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