Video-Based Human Action Recognition Using Spatial Pyramid Pooling and 3D Densely Convolutional Networks

Wanli Yang, Yimin Chen, Chen Huang and Mingke Gao
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Wanli Yang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Yimin Chen: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Chen Huang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Mingke Gao: The 32nd Research Institute, China Electronics Technology Group Corporation, No. 63 Chengliugong Road, Jiading District, Shanghai 200444, China

Future Internet, 2018, vol. 10, issue 12, 1-11

Abstract: In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area of video. It is well known that convolutional neural networks require a fixed size image input, which not only limits the network structure but also affects the recognition accuracy. Although this problem has been solved in the field of images, it has not yet been broken through in the field of video. To address the input problem of fixed size video frames in video recognition, we propose a three-dimensional (3D) densely connected convolutional network based on spatial pyramid pooling (3D-DenseNet-SPP). As the name implies, the network structure is mainly composed of three parts: 3DCNN, DenseNet, and SPPNet. Our models were evaluated on a KTH dataset and UCF101 dataset separately. The experimental results showed that our model has better performance in the field of video-based behavior recognition in comparison to the existing models.

Keywords: CNN; action recognition; spatial pyramid pooling; dense connectivity; 3D convolution (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
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