An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

Ahmed Dirir, Henry Ignatious, Hesham Elsayed, Manzoor Khan, Mohammed Adib, Anas Mahmoud and Moatasem Al-Gunaid
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Ahmed Dirir: College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates
Henry Ignatious: College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates
Hesham Elsayed: College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates
Manzoor Khan: College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates
Mohammed Adib: College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates
Anas Mahmoud: College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates
Moatasem Al-Gunaid: College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates

Future Internet, 2021, vol. 13, issue 12, 1-16

Abstract: Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

Keywords: object counting; object detection; multi-object tracking; deep learning; YOLO; correlation filters (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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