Omega Model for Human Detection and Counting for application in Smart Surveillance System
Subra Mukherjee, Karen Das

TL;DR
The paper introduces the Omega Model, a novel approach for human detection and counting in surveillance systems, utilizing four descriptors of head, neck, and shoulder features to achieve robustness under challenging conditions.
Contribution
It presents a new, robust model that employs four descriptors for human detection, effectively handling occlusion, background changes, and illumination variations.
Findings
The Omega Model achieves accurate human detection in diverse conditions.
Weighted decision making improves system performance.
Validation on real images confirms effectiveness.
Abstract
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…
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