CNNs Framework for Group Activity Recognition

Abstract

Human group activity recognition has drawn the attention of researchers worldwide because of the significant role it plays in many applications, including video surveillance and public security. To ensure high detection accuracy, current state-of-the-art tracking techniques require human supervision to identify objects of interest before automatic tracking can take place. Activity recognition in humans is one of the active challenges that finds its application in numerous fields such as, medical health care, military, manufacturing, assistive techniques and gaming. Due to the advancements in technologies the usage of smartphones in human lives become inevitable. The sensors in the smartphones help us to measure the essential vital parameters. These measured parameters enable us to monitor the activities of humans, which we call as human activity recognition. In this System, we present a framework based on convolutional neural networks (CNNs) for group activity recognition.