基于优化卷积神经网络(OCNN)的关键帧提取分析

T. Prabakaran, L. Kumar, S. Ashabharathi, S. Prabhavathi, Maneesh Vilas Deshpande, M. Fahlevi
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引用次数: 0

摘要

多媒体在视频中扮演着定时帧的角色。表示帧显示了视频清晰度的意图。关键帧是从视频帧中提取信息的重要因素。不相关的帧是寻找新密钥暴露的一个问题。在本文中,我们提出了一种使用优化卷积神经网络(OCNN)和强度特征选择(IFS)从运动捕捉数据中提取基本帧的新方法,以便更好地可视化和理解运动内容。它首先使用巴特沃斯滤波器去除运动捕捉数据中的噪声,然后通过主成分分析(PCA)减小噪声的大小。在主要分量中找到速度的零交叉,就得到了关键帧的初始集合。为了避免冗余,第一批重要帧被划分为相同的姿态。实验基于对动作捕捉数据库中帧的数据访问,实验结果表明,通过我们的方法检索的关键帧可以提高动作捕捉的可视化和理解能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Key Frame Extraction Analysis Based on Optimized Convolution Neural Network (OCNN) using Intensity Feature Selection (IFS)
The multimedia is playing role of timing frames in videos. The representation frame shows the intention on video definition. The keyframes the important factor for extraction information from video frames. The non-related frames is a problem for finding new key exposure. In this paper, we present a new method for extracting essential frames from motion capture data using Optimized Convolution Neural Network (OCNN) and Intensity Feature Selection (IFS) for better visualisation and understanding of motion content. It first removes noise from motion capture data using the Butterworth filter, then reduces the size via principal component analysis (PCA). Finding the zero-crosses of velocity in the main components yields the initial set of crucial frames. To avoid redundancy, the first batch of important frames is divided into identical poses. Experiments are based on data access from frames in the motion capture database, and experimental results suggest that crucial frames retrieved by our method can improve motion capture visualisation and comprehension.
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