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首页> 外文期刊>Intelligent Transport Systems, IET >Pedestrian motion recognition via Conv-VLAD integrated spatial-temporal-relational network
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Pedestrian motion recognition via Conv-VLAD integrated spatial-temporal-relational network

机译：通过Conv-VLAD集成空间 - 时间 - 关系网络行人运动识别

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摘要

Pedestrian motion recognition is one of the important components of an intelligent transportation system. Since commonly used spatial-temporal features are still not sufficient for mining deep information in frames, this study proposes a three-stream neural network called a spatial-temporal-relational network (STRN), where the static spatial information, dynamic motion and differences between adjunct keyframes are comprehensively considered as features of the video records. In addition, an optimised pooling layer called convolutional vector of locally aggregated descriptors layer (Conv-VLAD) is employed before the final classification step in each stream to better aggregate the extracted features and reduce the inter-class differences. To accomplish this, the original video records are required to be processed into RGB images, optical flow images and RGB difference images to deliver the respective information for each stream. After the classification result is obtained from each stream, a decision-level fusion mechanism is introduced to improve the network's overall accuracy via combining the partial understandings together. Experimental results on two public data sets UCF101 (94.7%) and HMDB51 (69.0%), show that the proposed method achieves significantly improved performance. The results of STRN have far-reaching significance for the application of deep learning in intelligent transportation systems to ensure pedestrian safety.
机译：行人运动识别是智能运输系统的重要组成部分之一。由于常用的空间 - 时间特征仍然不足以在帧中挖掘深深信息，因此提出了一种称为空间 - 时间 - 关系网络（Strn）的三流神经网络，其中静态空间信息，动态运动和差异附件密钥帧被全面被认为是视频记录的特征。另外，在每个流中的最终分类步骤之前，采用称为局部聚合描述符层（CONV-VLAD）的卷积矢量的优化池层，以更好地聚合提取的特征并降低级别的差异。为了实现这一点，要求原始视频记录被处理到RGB图像，光学流量图像和RGB差图像中以为每个流提供各个信息。在从每个流获得分类结果之后，引入了决策级融合机制，以通过将部分理解在一起来提高网络的整体精度。两种公共数据的实验结果集UCF101（94.7％）和HMDB51（69.0％），表明该方法实现了显着提高的性能。对智能交通系统中深度学习的应用具有深远的重要性，以确保行人安全性具有深远的意义。

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