Human Action Recognition Algorithm Based on Multi-Feature Map Fusion

Haofei Wang

doi:10.1109/ACCESS.2020.3017076

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Human Action Recognition Algorithm Based on Multi-Feature Map Fusion

2020-01-01

Haofei Wang

李

李俊峰 (Junfeng Li) *

DOI：10.1109/ACCESS.2020.3017076

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Abstract

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The emergence of the convolutional neural network greatly improves the accuracy of human action recognition. However, with the deepening of the network, fewer and fewer features are extracted, and in some datasets, due to the shooting angle, the size of the target to be recognized is different. To solve this problem, on the basis of resnext human action recognition method, we propose an improved resnext human action recognition method based on multi-feature map fusion. First, the video is uniformly sampled to generate training samples, and we generate samples with different frames as the input to the network. Second, we add n layers of up-sampling layers after layer 1 of resnext, to enlarge the feature maps and extract multiple feature maps, so that the extracted feature maps are clearer, and small targets can be better recognized. Finally, for the n results obtained, we use the weighted geometric means combination forecasting method based on L_1 norm to fuse and obtain the final result. In the process of experiment, using UCF-101 and HMDB-51 for verification, the accuracy of our model is 90.3% on UCF-101, which is higher than most of the state-of-art algorithms.

Keywords:

Feature extraction

Convolutional neural networks

Interpolation

Target recognition

Two dimensional displays

Three-dimensional displays

Network architecture

Human action recognition

resnext-101 network

up-sampling method

weight fusion

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