  Driven by the significant advancements in technology and social issues such
as security management, there is a strong need for Smart Surveillance System in
our society today. One of the key features of a Smart Surveillance System is
efficient human detection and counting such that the system can decide and
label events on its own. In this paper we propose a new, novel and robust
model, The Omega Model, for detecting and counting human beings present in the
scene. The proposed model employs a set of four distinct descriptors for
identifying the unique features of the head, neck and shoulder regions of a
person. This unique head neck shoulder signature given by the Omega Model
exploits the challenges such as inter person variations in size and shape of
peoples head, neck and shoulder regions to achieve robust detection of human
beings even under partial occlusion, dynamically changing background and
varying illumination conditions. After experimentation we observe and analyze
the influences of each of the four descriptors on the system performance and
computation speed and conclude that a weight based decision making system
produces the best results. Evaluation results on a number of images indicate
the validation of our method in actual situation.
