A Fall Detection Algorithm Based on Improved Convolutional Neural Network
This article addresses the problems of weak model generalization ability,low detection accuracy,and slow convergence speed due to the limited number of high-quality public fall datasets.A transfer learning method suitable for fall detection is designed,which replaces the fully connected layer method with a Global Average Pooling(GAP)layer and adds a Batch Normalization(BN)operation in the hidden layer to optimize network parameters,Multiple comparative experiments were conducted,and it was found that the improved network model had improved training time on different datasets compared to before,achieving good results.This enabled the neural network to learn both universal features on large-scale image datasets and fall features on publicly available drop datasets,enhancing the network's generalization ability.
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