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Research into
intelligent visual surveillance technologies has become one of the core
problem areas of the computer vision research community. The field
enjoys well-funded national and international funding programmes and
boasts a plethora of start-up companies offering a wide range of
intelligent CCTV products. Yet, despite the apparent success, the core
problems remain as complex and varied as any in computer vision. The
deployment of truly intelligent and robust surveillance systems face a
number of specific challenges including the segmentation and tracking of
individuals in crowded scenes; extracting body pose; characterization of
threats; reduction in false alarm rates; automatic learning of
human-oriented scene structure; and the semantic linkage between
networks of cameras and other sensors. To address these challenges,
contributions are particularly welcome in the following areas:
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