基于包卷积神经网络的人体运动识别

Yupeng Ding, Hongjun Li, Zhengyu Li
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引用次数: 1

摘要

为了解决输入数据混乱的问题,提出了一种基于包卷积神经网络的人体动作识别算法。采用两层小波结合均方误差法对样本进行分组,然后在保证分组误差的情况下对特征进行研究。在视频库上对该算法进行了测试,并与传统的卷积神经网络算法进行了比较。实验结果表明,与同类算法相比,本文提出的算法在主客观性能上均有显著提高,成功率有较大提高。
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Human motion recognition based on packet convolution neural network
In order to solve the confusion of input data, an algorithm of human action recognition based on packet convolution neural network is proposed. The two-layer wavelet combined with the mean square error method is used to group the samples, and then study the features in the case of guaranteeing the grouping error. The algorithm is tested on the video library and compared with the traditional convolution neural network algorithm. The experimental results show that the proposed algorithm has a significant improvement in the subjective and objective performance compared with the similar algorithm, and the success rate has been greatly improved.
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