A Robust Bi-Directional Algorithm For People Count In Crowded Areas

TL;DR

This paper introduces a robust bi-directional people counting algorithm based on blob assessment, designed for real-time crowded environments to accurately track inflow and outflow of individuals for safety and statistical purposes.

Contribution

The paper presents a novel bi-directional counting algorithm that improves accuracy and robustness in real-time crowded scenarios, focusing on inflow and outflow detection.

Findings

Effective in real-time crowded environments

Accurately distinguishes inbound and outbound people

Useful for safety and statistical analysis

Abstract

People counting system in crowded places has become a very useful practical application that can be accomplished in various ways which include many traditional methods using sensors. Examining the case of real time scenarios, the algorithm espoused should be steadfast and accurate. People counting algorithm presented in this paper, is centered on blob assessment, devoted to yield the count of the people through a path along with the direction of traversal. The system depicted is often ensconced at the entrance of a building so that the unmitigated frequency of visitors can be recorded. The core premise of this work is to extricate count of people inflow and outflow pertaining to a particular area. The tot-up achieved can be exploited for purpose of statistics in the circumstances of any calamity occurrence in that zone. Relying upon the count totaled, the population in that vicinity can be assimilated in order to take on relevant measures to rescue the people.