Notice: This lecture is a research seminar for credit.
September 25th, Tuesday, 2018 2:00-3:30 p.m.
Anomaly detection in videos is a challenging problem
in computer vision because only normal events are available in training set.
Most previous work handle the problem within a sparse representation framework:
a dictionary is learnt to minimize the reconstruction error for normal events
and abnormal events would lead to large reconstruction error. However such
sparse representation is computationally expensive in the testing phase.
Inspired the optimization of sparse representation, we propose to build a special
type of deep neural network, which is a counterpart of sparse coding. Then we
simplify the network which not only improves the speed but also accuracy.
Further, it is worth noting that anomaly detection refers to the identification
of events that do not conform to expected behavior, so we propose to solve
anomaly detection within future video frame prediction framework. By
simultaneously enforcing the spatial and temporal consistency of videos frames
of normal videos, we can predict high quality video frames for normal videos.
Extensive experiments validates the effectiveness of such video frame
prediction framework over feature reconstruction framework for anomaly
Shenghua Gao is an assistant professor in
Shanghai Tech University, China. He received the B.E. degree from the University
of Science and Technology of China in 2008, and received the Ph.D. degree from
the Nanyang Technological University in 2012. From Jun 2012 to Aug 2014, he
worked as a research scientist in UIUC Advanced Digital Sciences Center，Singapore. From Jan 2015 to June 2015, he
visited UC Berkeley as a visiting professor. His research interests include
computer vision and machine learning. He has published about 50 papers on image
and video understanding in many top-tier international conferences and
journals, including TPAMI,IJCV, TIP, TNNLS, CVPR, ICCV, etc.
Professors and students of TBSI are welcome to attend. The lecture is also open to the public. For off-campus personnel, please scan the QR code and and fill in your information (name, company, contact number, ID number). The language of the lecture is English.