Queen Mary Vision Laboratory seminar
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2PM at CS446 on Wednesday 10 Sept. 2008
Title: Joint Random Fields for Moving Vehicle Detection

Speaker: Dr Yang Wang from Neville Roach Laboratory, NICTA, Australia.
Abstract: This work proposes a joint random field (JRF) model for moving
vehicle detection in video sequences. The JRF model extends the
conditional random field (CRF) by introducing auxiliary latent variables
to characterize the structure and evolution of visual scene. Hence
detection labels (e.g. vehicle/roadway) and hidden variables (e.g. pixel
intensity under shadow) are jointly estimated to enhance vehicle
segmentation in video sequences. Data-dependent contextual constraints
among both detection labels and latent variables are integrated during
the detection process. The proposed method handles both moving cast
shadows/lights and background illumination variations. Computationally
efficient algorithm has been developed for real-time vehicle detection
in video streams.

Biography: Yang Wang is a researcher of Making Sense of Data group in
Neville Roach Laboratory. He received his PhD degree in computer science
from National University of Singapore in 2004. Before joining NICTA in
2006, he worked at Institute for Infocomm Research, Rensselaer
Polytechnic Institute, and Nanyang Technological University. Yang Wang
has published more than twenty international conference and journal
papers on artificial intelligence and computer vision. His current
research focuses on video based multi-object segmentation, tracking, and
analysis by machine learning and information fusion techniques.