Rui Li
ABSTRACT
The study of animal behavior can provide a basis for ecological researchers to help them formulate more reasonable and targeted ecological protection strategies. In this paper, the feature detection method based on cascade convolutional neural network is applied to the detection of animal features. By analyzing the general flow of convolutional neural networks, the researchers designed a cascade convolutional neural network with three hierarchical structures. Each hierarchical structure has multiple convolutional networks, which perform the same or different operations respectively. In order to make up for the defects of neural network in reasoning, this paper uses Bayesian network to infer and estimate in the analysis of features. Through experiments on the proposed method on a self-built data set, the results show that the overall accuracy of the animal behavior estimation method based on CCNN and BN proposed in this paper is more than 85%, and compared with general methods, this method has the advantages of fast learning speed, accurate feature location, and high accuracy of behavior estimation.
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Index Terms
- An Animal Behavior State Estimation Method Using CCNN and BN Based System
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