INTELLIGENT VISUAL SURVEILLANCE

In this book, we describe the proposed pedestrian
classification and tracking system that is
able to track and label multiple people in an
outdoor environment such as a railway station. We
propose an approach that combines blob matching with
particle filtering to track multiple people
in the scene, i.e., the proposed method selects the
successful features of blob matching and particle
filtering for tracking. In addition, a
novel appearance model derived from the colour
information from both the moving regions and
theoriginal input colour image is proposed to track
people in the event of poor foreground extraction.
Additionally, the proposed appearance model also
includes spatial information of the human
body in both vertical and horizontal directions,
making location more accurate. Furthermore, a novel
method to extract cars from moving regions including
shadow area, based
on shape and colour information, is proposed.
Chamfer template matching score, and non-shadow
region edge score, are applied as the shape
information; while the shadow confidence score
(SCS) is used as the colour information.

Autorentext

Suyu Kong received his Master of Philosophy degree in compute science from the University of Queensland. During his postgraduate period, he received the scholarship from National ICT, Australia(NICTA) and also worked there as a reserch student. His papers about people tracking for visual surveillance have been accepted by AVSS and ICPR.

Klappentext

In this book, we describe the proposed pedestrian classification and tracking system that isable to track and label multiple people in an outdoor environment such as a railway station. Wepropose an approach that combines blob matching with particle filtering to track multiple peoplein the scene, i.e., the proposed method selects the successful features of blob matching and particlefiltering for tracking. In addition, anovel appearance model derived from the colour information from both the moving regions and theoriginal input colour image is proposed to track people in the event of poor foreground extraction.Additionally, the proposed appearance model also includes spatial information of the humanbody in both vertical and horizontal directions, making location more accurate. Furthermore, a novel method to extract cars from moving regions including shadow area, basedon shape and colour information, is proposed. Chamfer template matching score, and non-shadowregion edge score, are applied as the shape information; while the shadow confidence score(SCS) is used as the colour information.