SDL-Net:一个CNN和RNN相结合的人类活动识别模型

D. Gupta, Ananya Komal Singh, Naman Gupta, D. Vishwakarma
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引用次数: 0

摘要

人体动作识别在研究人员和科学家中非常受欢迎,被认为是计算机视觉领域最活跃的应用之一。它在医疗保健、监控、体育等许多领域的现代应用中非常有用。深度学习为以最简单的方式预测人类行为提供了动力。本文提出了一种结合CNN和RNN的人体动作识别模型SDL-Net,该模型利用部分亲和场(paf)生成骨骼表征,并生成骨骼步态能量图像。它还捕获顺序模式以生成顺序数据。在Kinect活动识别数据集(KARD)上进行了实验,取得了理想的结果,显示了该方法的效率和有效性。
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SDL-Net: A Combined CNN & RNN Human Activity Recognition Model
Human Action Recognition is quite popular among researchers and scientists and is considered one of the most active applications in the field of computer vision. It is quite useful in modern era applications like healthcare, surveillance, sports and many more fields. Deep Learning has provided an upliftment to predict human actions in an easiest way possible. This paper proposes a combined CNN & RNN human action recognition model named SDL-Net, which generates skeletal representations using Part Affinity Fields (PAFs) and generates skeletal gait energy images. It also captures sequential patterns to generate sequential data as well. Experiments are conducted on Kinect Activity Recognition Dataset (KARD) and it shows the efficiency and effectiveness by achieving desirable results.
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