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SYSTEM AND METHOD FOR HUMAN ACTION RECOGNITION AND INTENSITY INDEXING FROM VIDEO STREAM USING FUZZY ATTENTION MACHINE LEARNING

机译:使用模糊注意力机器学习从视频流的人类行动识别和强度索引的系统和方法

摘要

A system and method are provided for accurately recognizing actions and estimating action intensity. To enable the system to deal with the uncertainty and varied nature inherent in action recognition and intensity indexing, the system is a hybrid system that combines the concept of fuzzy logic and deep recurrent neural networks. The methodology is an attentive neuro-fuzzy system designed to recognize qualitative differences in human actions and to self-adapt to different intensities. The model of the system and method utilizes recurrent neural networks to detect actions from spatio-temporal patterns of human poses, in tandem with an adaptive fuzzy inference system to learn the various human motions used to perform actions with different intensities and then estimate the action's intensity. The integrated model can successfully learn the unique way a specific action with a certain intensity is performed and can estimate the intensity of the respective action.
机译:提供了一种用于准确识别动作和估计动作强度的系统和方法。 为了使系统能够处理行动识别和强度索引中固有的不确定性和变化性质,系统是一个混合系统,它结合了模糊逻辑和深度经常性神经网络的概念。 该方法是一部分的神经模糊系统,旨在识别人类行为的定性差异,并自适应对不同的强度。 系统和方法的模型利用反复性神经网络来检测人类姿势的时空模式的动作,与自适应模糊推理系统一起学习用于执行不同强度的各种人类运动,然后估计动作的强度。然后估计动作的强度 。 集成模型可以成功地学习具有特定动作的独特方式,执行特定的强度,并且可以估计各个动作的强度。

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