Human Shape Variation - An Efficient Implementation using Skeleton

TL;DR

This paper introduces a fast, robust human shape recognition algorithm using skeleton-based features to detect human presence in security environments efficiently.

Contribution

It presents a novel skeleton-based shape recognition method that is simple, efficient, and suitable for real-time security applications.

Findings

Algorithm is fast and robust in detecting humans.

Uses skeletons, posture, and length features for recognition.

Suitable for real-time security monitoring.

Abstract

It is at times important to detect human presence automatically in secure environments. This needs a shape recognition algorithm that is robust, fast and has low error rates. The algorithm needs to process camera images quickly to detect any human in the range of vision, and generate alerts, especially if the object under scrutiny is moving in certain directions. We present here a simple, efficient and fast algorithm using skeletons of the images, and simple features like posture and length of the object.