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Human Action Recognition Algorithm Based on Multi-Feature Map Fusion
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DOI：10.1109/ACCESS.2020.3017076.png)
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摘要
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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.
Keyword:
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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3.6
论文数:
9.8W
被引数:
29.4W
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