Abstract: Humans are the best teachers as well as beneficiaries of AI development. My human-centred visual learning research is to develop vision-based algorithms that aim to make systems usable and useful by focusing on humans, especially their needs and requirements. In particular, understanding human body pose/hand/gaze is fundamental for meaningful interpretation of human action and behaviour. In this talk, I will introduce my recent research on human-centred vision tasks, including attention driven image cropping, real-time hand pose & gaze tracking, and human body movement retargeting, and so forth. Also, some interesting applications using the human-centred vision methods will be presented.
 
Bio: Dr. Hyung Jin Chang is an Associate Professor in the school of computer science at the University of Birmingham and a Turing Fellow of the Alan Turing Institute. Before joining the University of Birmingham, he was a post-doctoral researcher at Imperial College London and received his PhD degree from Seoul National University. His research combines multiple artificial intelligence areas, including computer vision, machine learning, robotics, and human-computer interaction. His research career started focusing on theoretical aspects of machine learning, and it has converged on applying these aspects to more practical problems in visual surveillance, HCI, and robotics, with an emphasis on estimating human eye gaze, hand pose, body pose, kinematic structure, and 6D object pose etc. Recently, his research has focused on exploiting and making advances in computer vision and learning techniques to move toward intelligent human-robot/human-computer interaction based on visual data.