Multi-scale residual aggregation feature network based on multi-time division for motion behavior recognition

Abstract

The existing behavior recognition models based on the deep convolutional neural network have some problems, such as feature extraction with a single scale and insufficient feature utilization in the middle level. In this paper, we propose a multi-scale residual aggregation feature network based on multi-time division for behavior recognition. Through the sampling form of multi-time division, the diversity of behavior depth features is enriched. Firstly, a hybrid extended convolution residual block (HERB) is designed using extended convolution and residual join with different extension coefficients to extract feature information at multiple scales effectively. Secondly, a feature aggregation mechanism (AM) is introduced to solve the problem of insufficient feature utilization in the middle layer of the network. We construct a deep aggregation model that can learn the distribution of complex behavior features to solve the problem of human behavior classification over a long time span. Experiments on behavioral datasets UCF101 and HMDB51 verify the effectiveness of the new algorithm.

KEYWORDS:

• Motion behavior recognition
• deep convolutional neural network
• multi-time division
• HERB
• aggregation mechanism

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Fang Duan

Fang Duan is with the College of Tai Chi Martial Arts, Jiaozuo University, Jiaozuo City 454000, China. She is a lecturer. Her interests include motion analysis, image processing.