Human Motion Recognition Based on Improved 3-Dimensional Convolutional Neural Network

In recent years, deep Convolutional neural networks(CNNs) have made fantastic progress in static image recognition, but the ability to model motion information on behavioral video is weak. Therefore, our paper put forward a new time transition layer that models variable temporal convolution kernel depths. We embed this new Hybrid Model in our proposed 3D CNN. We extend the DenseNet architecture with 3D filters and pooling kernels. It will take time as training a 3D convolutional neural network requires a large number of tagged data sets to start training from the input. Therefore, the focus of this paper is on simple and effective technique of passing 2D convolutional neural network pre-trained data to a randomly initialized 3D convolutional neural network for stable weight initialization, where we can still achieve our experimental results by appropriately reducing the number of 3D convolutional neural network training samples. Experiments show that the network can make a more accurate classification of behavioral video, identify it in the UCF-101 database, and compare it with other classical algorithms that have appeared in recent years. The results reflect the superiority of the algorithm.