  Recognizing human actions based on videos has became one of the most popular
areas of research in computer vision in recent years. This area has many
applications such as surveillance, robotics, health care, video search and
human-computer interaction. There are many problems associated with recognizing
human actions in videos such as cluttered backgrounds, obstructions, viewpoints
variation, execution speed and camera movement. A large number of methods have
been proposed to solve the problems. This paper focus on spatial and temporal
pattern recognition for the classification of videos using Deep Neural
Networks. This model takes RGB images and Optical Flow as input data and
outputs an action class number. The final recognition accuracy was about 94%.
