ECPNet: An Efficient AttentionBased Convolution Network with Pseudo3D Block for Human Action Recognition Human action recognition has became an important task in computer vision and has received a significant amount of research interests in recent years. Convolutional Neural Network (CNN) has shown its power in image recognition task. While in the field of video recognition, it is still a challenge problem. In this paper, we introduce a highefficient attentionbased convolutional network named ECPNet for video understanding. ECPNet adopts the framework that is a consecutive connection of 2D CNN and pseudo3D CNN. The pseudo3D means we replace the traditional 3 xc3x97 3 xc3x97 3 kernel with two 3D convolutional filters shaped 1 xc3x97 3 xc3x97 3 and 3 xc3x97 1 xc3x97 1. Our ECPNet combines the advantages of both 2D and 3D CNNs: (1) ECPNet is an endtoend network and can learn information of appearance from images and motion between frames. (2) ECPNet requires less computing resource and lower memory consumption than many stateofart models. (3) ECPNet is easy to expand for different demands of runtime and classification accuracy. We evaluate the proposed model on three popular video benchmarks in human action recognition task: Kineticsmini (split of full Kinetics), UCF101 and HMDB51. Our ECPNet achieves the excellent performance on above datasets with less time cost.

A Spatiotemporal Hybrid Network for Action Recognition Convolutional Neural Networks (CNNs) are powerful in learning spatial information for static images, while they appear to lose their abilities for action recognition in videos because of the neglecting of longterm motion information. Traditional 3D convolution has high computation complexity and the used Global Average Pooling (GAP) on the bottom of network can also lead to unwanted content loss or distortion. To address above problems, we propose a novel action recognition algorithm by effectively fusing 2D and Pseudo3D CNN to learn spatiotemporal features of video. First, we use Pseudo3D CNN with proposed Multilevel pooling module to learn spatiotemporal features. Second, the features output by multilevel pooling module are passed through our proposed processing module to make full use of the rich features. Third, a 2D CNN fed with motion vectors is designed to extract motion patterns, which can be regarded as a supplement of Pseudo3D CNN to make up for the information lost by RGB images. Fourth, a dependencybased fusion method is proposed to fuse the multistream features. Finally, the effectiveness of our proposed action recognition algorithm is demonstrated on public UCF101 and HMDB51 datasets.