Prof. Xiaogang Wang, The Chinese University of Hong Kong
Date and Time: 9:00 – 10:00 am, Saturday, February 20, 2016
Venue: Room 146 Research Building
Title: Deep Learning in Video Surveillance
This talk will introduce our recent deep learning works in video surveillance. The applications of deep learning include object detection, pedestrian detection, person re-identification, general object tracking, crowd segmentation, crowd density estimation, crowd counting and crowd video classification. Many results have shown that deep learning can advance the state-of-the-art of video surveillance substantially. The focus of this talk would be the strategies of designing network structures and learning feature representations adapting to surveillance applications. With carefully designed network structures and training schemes, the learned features could be effective for general objects, a particular object class, a particular object instance, or a large group of people to fulfill the requirements of different surveillance applications. In video surveillance, it is also critical for the feature representations to be robust across a large number of diversified scenes and camera views, to be robust to background clutters, and to well motion information. Deep learning is effective on addressing these challenges.
Xiaogang Wang received his Bachelor degree in Electronic Engineering and Information Science from the Special Class of Gifted Young at the University of Science and Technology of China in 2001, M. Phil. degree in Information Engineering from the Chinese University of Hong Kong in 2004, and PhD degree in Computer Science from Massachusetts Institute of Technology in 2009. He is an associate professor in the Department of Electronic Engineering at the Chinese University of Hong Kong since August 2009. He received the Outstanding Young Researcher in Automatic Human Behaviour Analysis Award in 2011, Hong Kong RGC Early Career Award in 2012, and Young Researcher Award of the Chinese University of Hong Kong. Recently, his team won the challenge of object detection in videos at ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2015. He is the associate editor of the Image and Visual Computing Journal. He was the area chair of ICCV 2011 and 2015, ECCV 2014 and 2016, ACCV 2014 and 2016. His research interests include computer vision, deep learning, crowd video surveillance, object detection, and face recognition.