An action recognition method based on neural architecture search

Abstract

Facing complex and diverse action recognition scenarios, artificially designed neural networks show poor generalization performance. Therefore, an automatic designing method of 3D convolutional neural networks based on neural architecture search is proposed. Firstly, a variety of human behaviors are extracted to construct training sets and validation sets. Additionally, the weights of neuron connections are updated using the loss on the training set, the discrete network architecture search space is continuous through continuous relaxation, and the search space is reduced by using the hierarchical idea. What’s more, the objective function is optimized by combining gradient descent, realizing fast search, and stacking the obtained computing units to form an overall network at the same time. Evaluations based on public data sets show that the designed neural network model achieves comparable performance to the artificially designed network model in the task of human action recognition, solving the difficulty of deep learning method migration between working at different scenarios by automatically customizing the network.