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Efficient video streaming using gesture recognition in small class peer to peer e-learning

Synchronous peer to peer e-learning has recently been receiving more popularity, as instructor and students can communicate directly in real-time through exchanging multimedia, particularly, the audiovisual data. Since most peer to peer e-learning applications are PC based and participants are connected through the internet, live video feeds therefore have consumed a lot of bandwidth, creating excessive network traffic. In our work, we have recognized the trend in applying advanced computer vision and pattern recognition (CVPR) technologies to automate real time e-learning activities. Following this trend, we discuss the use of fast pose recognition methods in both instructor and student’s side. Based on these methodologies, we proposed an intelligent framework which uses pose information to effectively control the video streaming.