A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM

Mahwish Pervaiz,Munkhjargal Gochoo,Shaharyar Kamal,Yazeed Yasin Ghadi,Dong-Seong Kim,Ahmad Jalal

doi:10.3390/su13105367

Abstract

Based on the rapid increase in the demand for people counting and tracking systems for surveillance applications, there is a critical need for more accurate, efficient, and reliable systems. The main goal of this study was to develop an accurate, sustainable, and efficient system that is capable of error-free counting and tracking in public places. The major objective of this research is to develop a system that can perform well in different orientations, different densities, and different backgrounds. We propose an accurate and novel approach consisting of preprocessing, object detection, people verification, particle flow, feature extraction, self-organizing map (SOM) based clustering, people counting, and people tracking. Initially, filters are applied to preprocess images and detect objects. Next, random particles are distributed, and features are extracted. Subsequently, particle flows are clustered using a self-organizing map, and people counting and tracking are performed based on motion trajectories. Experimental results on the PETS-2009 dataset reveal an accuracy of 86.9% for people counting and 87.5% for people tracking, while experimental results on the TUD-Pedestrian dataset yield 94.2% accuracy for people counting and 94.5% for people tracking. The proposed system is a useful tool for medium-density crowds and can play a vital role in people counting and tracking applications.

Highlights

People counting and tracking in public places has emerged as a hot topic with increases in the global population
We considered the problem of object occlusion and attempted to minimize this issue by clustering particle flows (PFs)
The proposed method consists of seven main steps: preprocessing, object detection, people verification, PF and feature extraction, self-organizing map (SOM)-based clustering, people counting, and people tracking

Summary

Introduction

People counting and tracking in public places has emerged as a hot topic with increases in the global population This is because population increases have led to a major increase in mass events. People have a significant interest in events such a carnival, sports [1,2,3,4], concerts, and festivals [5] Such events require proper safety measures and crowd management. People counting and tracking systems are used to estimate crowd density and track human activities in crowded areas. Such systems can be used to manage transport systems, schedule labor, monitor promotional events, and improve surveillance systems [6,7]

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Methods

Discussion

Conclusion