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

2018 ◽  
Vol 10 (12) ◽  
pp. 115
Author(s):  
Wanli Yang ◽  
Yimin Chen ◽  
Chen Huang ◽  
Mingke Gao
Keyword(s):  
Neural Networks ◽  
Network Structure ◽  
Three Dimensional ◽  
Human Action Recognition ◽  
Human Action ◽  
Fixed Size ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

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.

Human Action Recognition Combining Sequential Dynamic Images and Two-Stream Convolutional Network

2021 ◽  
Vol 58 (2) ◽  
pp. 0210007
Author(s):  
张文强 Zhang Wenqiang ◽  
王增强 Wang Zengqiang ◽  
张良 Zhang Liang
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Convolutional Network

scholarly journals Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching

2020 ◽  
Vol 34 (03) ◽  
pp. 2669-2676 ◽  
Author(s):  
Wei Peng ◽  
Xiaopeng Hong ◽  
Haoyu Chen ◽  
Guoying Zhao
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Order Approximation ◽  
Human Action Recognition ◽  
Search Space ◽  
Human Action ◽  
Higher Order ◽  
Dynamic Graph ◽  
Convolutional Network ◽  
Representational Capacity

Human action recognition from skeleton data, fuelled by the Graph Convolutional Network (GCN) with its powerful capability of modeling non-Euclidean data, has attracted lots of attention. However, many existing GCNs provide a pre-defined graph structure and share it through the entire network, which can loss implicit joint correlations especially for the higher-level features. Besides, the mainstream spectral GCN is approximated by one-order hop such that higher-order connections are not well involved. All of these require huge efforts to design a better GCN architecture. To address these problems, we turn to Neural Architecture Search (NAS) and propose the first automatically designed GCN for this task. Specifically, we explore the spatial-temporal correlations between nodes and build a search space with multiple dynamic graph modules. Besides, we introduce multiple-hop modules and expect to break the limitation of representational capacity caused by one-order approximation. Moreover, a corresponding sampling- and memory-efficient evolution strategy is proposed to search in this space. The resulted architecture proves the effectiveness of the higher-order approximation and the layer-wise dynamic graph modules. To evaluate the performance of the searched model, we conduct extensive experiments on two very large scale skeleton-based action recognition datasets. The results show that our model gets the state-of-the-art results in term of given metrics.

Sign in / Sign up

Export Citation Format

Share Document