A Novel Equation based Classifier for Detecting Human in Images

TL;DR

This paper introduces a new shape-based classifier using the Omega Equation to detect humans in images by analyzing head-shoulder shapes, addressing challenges of variability in appearance.

Contribution

The paper presents a novel Omega Equation and a shape classifier specifically designed for human detection based on head-shoulder shape analysis.

Findings

Successfully detects humans across varied head-shoulder shapes

Demonstrates satisfactory results on diverse shape datasets

Addresses challenges of shape variability in human detection

Abstract

Shape based classification is one of the most challenging tasks in the field of computer vision. Shapes play a vital role in object recognition. The basic shapes in an image can occur in varying scale, position and orientation. And specially when detecting human, the task becomes more challenging owing to the largely varying size, shape, posture and clothing of human. So, in our work we detect human, based on the head-shoulder shape as it is the most unvarying part of human body. Here, firstly a new and a novel equation named as the Omega Equation that describes the shape of human head-shoulder is developed and based on this equation, a classifier is designed particularly for detecting human presence in a scene. The classifier detects human by analyzing some of the discriminative features of the values of the parameters obtained from the Omega equation. The proposed method has been tested on a variety of shape dataset taking into consideration the complexities of human head-shoulder shape. In all the experiments the proposed method demonstrated satisfactory results.