Multi-features descriptors for Human Activity Tracking and Recognition in Indoor-Outdoor Environments

Tracking human activities and analyzing their effect in real life settings has become a task of high interest within the computer vision field as it enable many industrial and commercial applications. Sensors and communication technologies are being used for capturing human movements and providing interactive interfaces for man-machine collaboration. However, introducing intelligent computing for better scene understanding is still an unexplored domain for the researchers. In this paper, we have made an effort to allow machines understand the behaviors in outer environment by proposing a novel methodology to recognize human interactions. The objective of this research is to embed cognitive processes in information technologies for exploring new directions of intelligent media. Our proposed human activity recognition (HAR) system recognize eight complex human activities taken from BIT- Interaction dataset i.e. bow, boxing, handshake, high-five, hug, kick, pat and push. We have designed multiple features algorithms along with convolutional neural network (CNN) to evaluate the performance of our system compared with other state of the art classifiers. While, experimental results show that the proposed methodology is reliable at complex realistic settings and applicable in security systems, e-learning, smart surveillance, violence detection, child abuse protection, elderly care, virtual games, intelligent video retrievals and human computer interaction.