As networks of video cameras are installed in many applications like security and surveillance
environmental monitoring disaster response and assisted living facilities among others
image understanding in camera networks is becoming an important area of research and technology
development. There are many challenges that need to be addressed in the process. Some of them
are listed below: - Traditional computer vision challenges in tracking and recognition
robustness to pose illumination occlusion clutter recognition of objects and activities -
Aggregating local information for wide area scene understanding like obtaining stable
long-term tracks of objects - Positioning of the cameras and dynamic control of pan-tilt-zoom
(PTZ) cameras for optimal sensing - Distributed processing and scene analysis algorithms -
Resource constraints imposed by different applications like security and surveillance
environmental monitoring disaster response assisted living facilities etc. In this book we
focus on the basic research problems in camera networks review the current state-of-the-art
and present a detailed description of some of the recently developed methodologies. The major
underlying theme in all the work presented is to take a network-centric view whereby the
overall decisions are made at the network level. This is sometimes achieved by accumulating all
the data at a central server while at other times by exchanging decisions made by individual
cameras based on their locally sensed data. Chapter One starts with an overview of the problems
in camera networks and the major research directions. Some of the currently available
experimental testbeds are also discussed here. One of the fundamental tasks in the analysis of
dynamic scenes is to track objects. Since camera networks cover a large area the systems need
to be able to track over such wide areas where there could be both overlapping and
non-overlapping fields of view of the cameras as addressed in Chapter Two: Distributed
processing is another challenge in camera networks and recent methods have shown how to do
tracking pose estimation and calibration in a distributed environment. Consensus algorithms
that enable these tasks are described in Chapter Three. Chapter Four summarizes a few
approaches on object and activity recognition in both distributed and centralized camera
network environments. All these methods have focused primarily on the analysis side given that
images are being obtained by the cameras. Efficient utilization of such networks often calls
for active sensing whereby the acquisition and analysis phases are closely linked. We discuss
this issue in detail in Chapter Five and show how collaborative and opportunistic sensing in a
camera network can be achieved. Finally Chapter Six concludes the book by highlighting the
major directions for future research. Table of Contents: An Introduction to Camera Networks
Wide-Area Tracking Distributed Processing in Camera Networks Object and Activity
Recognition Active Sensing Future Research Directions
