Human Action Recognition using Pre-trained Convolutional Neural Networks

Abstract

Recognition of human action is one of the challenges in the field of artificial intelligence. Deep learning model has become a research issue in action recognition applications due to its ability to outperform traditional machine learning approaches. The Convolutional Neural Network is one of the architectures commonly used in most action recognition works. There are different models in the Convolutional Neural Network, but no study has been done to evaluate which model has the best performance in understanding human actions. Thus, in this paper, we compare the performance of two separate pre-trained models of deep Convolutional Neural Network in classifying the human actions to identify the different behaviours. GoogleNet and AlexNet are the used two models with fine-tuned parameters used for comparison, in addition, to use Long-Short Term Memory for the video's labels prediction. The paper's main contribution is that it offers a performance analysis of two separate fine-tuned deep CNN pre-trained models compared to the results of other recently proposed human action recognition methods applied on KTH, Weizmann, UCF11(YouTube actions) and UCF-Sports datasets.