TY - JOUR
T1 - A smart surveillance system for people counting and tracking using particle flow and modified som
AU - Pervaiz, Mahwish
AU - Ghadi, Yazeed Yasin
AU - Gochoo, Munkhjargal
AU - Jalal, Ahmad
AU - Kamal, Shaharyar
AU - Kim, Dong Seong
N1 - Funding Information:
Funding: This research was supported by the IITP (Institute for Information & Communications Technology Planning & Evaluation) (IITP-2020-2020-0-01612), and the National Research Foundation of Korea (NRF) (2018R1A6A1A03024003).
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/5/2
Y1 - 2021/5/2
N2 - 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.
AB - 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.
KW - Clustering
KW - Modified self-organizing map
KW - Object detection
KW - Particle flow
KW - People counting and tracking
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U2 - 10.3390/su13105367
DO - 10.3390/su13105367
M3 - Article
AN - SCOPUS:85106619496
VL - 13
JO - Sustainability
JF - Sustainability
SN - 2071-1050
IS - 10
M1 - 5367
ER -