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

Mahwish Pervaiz, Yazeed Yasin Ghadi, Munkhjargal Gochoo, Ahmad Jalal, Shaharyar Kamal and Dong-Seong Kim
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Mahwish Pervaiz: Department of Computer Science, Bahria University, Islamabad 44000, Pakistan
Yazeed Yasin Ghadi: Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi 122612, United Arab Emirates
Munkhjargal Gochoo: Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates
Ahmad Jalal: Department of Computer Science, Air University, Islamabad 44000, Pakistan
Shaharyar Kamal: Department of Computer Science, Air University, Islamabad 44000, Pakistan
Dong-Seong Kim: Department of IT Convergence Engineering, Kumoh National Institute of Technology, Yanghodong 730-701, Korea

Sustainability, 2021, vol. 13, issue 10, 1-20

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.

Keywords: clustering; modified self-organizing map; object detection; particle flow; people counting and tracking (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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