Automated Lecturer-Tracking System


Development of technology has brought some significant changes to the educational system, resulting in some new means of gathering the knowledge. In this project, we focus on the video lectures that provide significant benefits for all the students. We aim to enhance the way video lessons are recorded by introducing the system for automatic lecturer-tracking. This thesis introduces the new approach in implementing an automated lecturer-tracking system by using a smartphone as the replacement for a camera device and a processing unit. The proposed solution uses the YOLO real-time object detection system and tracking algorithms from iOS Vision framework to detect and track the lecturer. A motorized pan-tilt head rotates the smartphone based on the input the smartphone sends to it. Experimental results show that the system can perform the desired behavior of lecturer-tracking, eliminating the need for human help in the process of recording.