Deep learning approach for human action recognition using Gated Recurrent Unit neural networks and motion analysis

Abstract

Human action recognition is a computer vision task. The evaluation of action recognition algorithms relies on the proper extraction and learning of the data. The success of the deep learning and especially learning layer by layer led to many imposing results in several contexts that include neural network. Here the Recurrent Neural Networks (RNN) with hidden unit has demonstrated advanced performance on tasks as varied as image captioning and handwriting recognition. Specifically Gated Recurrent Unit (GRU) is able to learn and take advantage of sequential and temporal data required for video recognition. Moreover video sequence can be better described on both visual and moving features. In this paper, we present our approach for human action recognition based on fusion and combination of sequential visual features and moving path. We evaluate our technique on the challenging UCF Sports Action, UCF101 and KTH dataset for human action recognition and obtain competitive results.