Omega Model for Human Detection and Counting for application in Smart Surveillance System

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 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.