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

• Published in: Future internet Vol. 10; no. 12; p. 115
• Main Authors: Yang, Wanli, Chen, Yimin, Huang, Chen, Gao, Mingke
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2018
• Subjects: 3D convolution; Accuracy; action recognition; Artificial intelligence; Artificial neural networks; Classification; CNN; dense connectivity; Human activity recognition; Human behavior; Human motion; Information seeking behavior; International conferences; Internet; Neural networks; Object recognition; Pattern recognition; spatial pyramid pooling; Success; Surveillance; Canada; United States > US; China
• DOI: 10.3390/fi10120115
• ISSN: 1999-5903

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.