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首页> 外文期刊>Journal of visual communication & image representation >Human motion quality assessment toward sophisticated sports scenes based on deeply-learned 3D CNN model
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Human motion quality assessment toward sophisticated sports scenes based on deeply-learned 3D CNN model

机译：基于深受学习的3D CNN模型对复杂体育场景的人为运动质量评估

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摘要

Video may be subject to various distortions during acquisition, processing, compression, storage, transmission, and reproduction, and it results in reduced visual quality. In complex sports scenes under big data environment, the human body's movements are even more so. The quality of human motion can intuitively affect the human visual experience. Therefore, it is necessary to determine an intelligent quality assessment model to evaluate human motion in complex motion scenarios under big data environment. It can be used to dynamically monitor and adjust video quality, and it can be used for algorithms and parameter settings in motion image processing systems. With the popularity of deep learning, convolutional neural networks have become a very important method in the field of computer vision research. Based on the 2D-CNN algorithm, we propose a 3D convolutional neural network model for human motion quality assessment in complex motion scenarios. The model captures the pose characteristics, motion trajectory, video brightness and contrast in time and space. The model feeds back the reference and distorted video pairs into the network, with each output layer acting as a feature map. The local similarity between the feature maps obtained from the reference video and the distorted video is then calculated and combined to obtain a global image quality score. Experiments show that the model can achieve competitive performance in big data environment for video quality assessment. (c) 2020 Elsevier Inc. All rights reserved.
机译：视频可以在采集，处理，压缩，存储，传输和再现期间经受各种扭曲，并且它导致视觉质量降低。在大数据环境下复杂的体育场景中，人体的动作甚至更加。人类运动的质量可以直观地影响人类的视觉体验。因此，有必要确定智能质量评估模型，以评估大数据环境下复杂运动场景中的人类运动。它可用于动态监控和调整视频质量，可用于运动图像处理系统中的算法和参数设置。随着深度学习的普及，卷积神经网络已经成为计算机视觉研究领域的一个非常重要的方法。基于2D-CNN算法，我们为复杂运动场景中的人类运动质量评估提出了一种3D卷积神经网络模型。该模型在时间和空间中捕获姿势特征，运动轨迹，视频亮度和对比度。该模型将参考和扭曲的视频对馈送到网络中，每个输出层充当特征图。然后计算从参考视频和失真视频获得的特征映射之间的局部相似性并组合以获得全局图像质量分数。实验表明，该模型可实现大数据环境中的竞争性能进行视频质量评估。 （c）2020 Elsevier Inc.保留所有权利。

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