3D CNN for Human Action Recognition

Recognizing different human actions from still images or videos is an important research area in the computer vision and artificial intelligence domains. It represents a key step for a wide range of applications including: human-computer interaction, ambient assisted living, intelligent driving and video surveillance. However, unless the many research works being involved, there are still many challenges ahead including: the high changes in human body shapes, clothing and viewpoint changes and the conditions of system acquisition (illumination variations, occlusions, etc.). With the emergence of new deep learning techniques, many approaches are recently proposed for Human Action Recognition (HAR). Compared with conventional machine learning methods, deep learning techniques have more powerful learning ability. The most wide-spread deep learning approach is the Convolutional Neural Network (CNN/ConvNets). It has shown remarkable achievements due to its precision and robustness. As a branch of neural network, 3D CNN is a relatively new technique in the field of deep learning. In this paper, we propose a HAR approach based on a 3D CNN model. We apply the developed model to recognize human actions of KTH and J-HMDB datasets, and we achieve state of the art performance in comparison to baseline methods.