Modified recurrent convolutional neural networks for action recognition

In this study, two new neural networks are proposed for action recognition. The first method is named as modified recurrent CNN and the second method is named as feature concatenating CNN. The main aim for proposing these networks is developing new techniques for sharing features between different frames of the videos. These methods have been applied on different datasets. Those datasets are UCF101, Hollywood2 and HMDB51. Based on the results, modified recurrent CNN is a good alternative for facilitating better feature learning. In most of the cases, accuracies provided by the modified recurrent neural networks are a few percent larger than accuracies provided by the standard convolutional neural networks. Additionally, a detailed review of the most important action recognition methods that are based on deep learning has been provided.