计算机科学
动作(物理)
动作识别
人工智能
卷积(计算机科学)
卷积神经网络
人工神经网络
计算机视觉
感知
视觉对象识别的认知神经科学
模式识别(心理学)
图像(数学)
特征提取
物理
量子力学
神经科学
生物
班级(哲学)
作者
Sung‐Joo Park,Dongchil Kim
标识
DOI:10.1109/icufn55119.2022.9829604
摘要
In the video surveillance system operating in the real environment, the recognition of detailed behavior of objects has important meaning in terms of understanding whether security events have occurred. In particular, the perception of behavior in a poor environment such as low light and overlapped objects is recognized as an important technical factor that must be overcome in the existing 2D image-based surveillance system. Action recognition of objects using 3D depth map provides a way to solve these issues. In this paper, we propose the enhanced 3D action recognition method based on convolution neural network (CNN) for the video surveillance system. And we evaluated the action recognition performance using the real environment DB, and the recognition result for 6 detailed behaviors was confirmed to be an average of 68.56%.
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