TY - JOUR
T1 - Smart Traffic Monitoring Through Pyramid Pooling Vehicle Detection and Filter-Based Tracking on Aerial Images
AU - Rafique, Adnan Ahmed
AU - Al-Rasheed, Amal
AU - Ksibi, Amel
AU - Ayadi, Manel
AU - Jalal, Ahmad
AU - Alnowaiser, Khaled
AU - Meshref, Hossam
AU - Shorfuzzaman, Mohammad
AU - Gochoo, Munkhjargal
AU - Park, Jeongmin
N1 - Funding Information:
This work was supported in part by the Basic Science Research Program through the National Research Foundation (NRF) funded by the Ministry of Education, Republic of Korea, under 2021R1F1A1063634; in part by the Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, through Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R235), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; in part by the Taif University, Taif, Saudi Arabia, through the Taif University Researchers Supporting Project under Grant TURSP-2020/79; and in part by the Emirates Center for Mobility Research (ECMR), United Arab Emirates University, Al Ain, under Grant #12R012.
Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - Increased traffic density, combined with global population development, has resulted in increasingly congested roads, increased air pollution, and increased accidents. Globally, the overall number of automobiles has expanded dramatically during the last decade. Traffic monitoring in this environment is undoubtedly a significant difficulty in various developing countries. This work introduced a novel vehicle detection and classification system for smart traffic monitoring that uses a convolutional neural network (CNN) to segment aerial imagery. These segmented images are examined to further detect the vehicles by incorporating novel customized pyramid pooling. Then, these detected vehicles are classified into various subcategories. Finally, these vehicles are tracked via Kalman filter (KF) and kernelized filter-based techniques to cope with and manage massive traffic flows with minimal human intervention. During the experimental evaluation, our proposed system illustrated a remarkable vehicle detection rate of 95.78% over the Vehicle Aerial Imagery from a Drone (VAID), 95.18% over the Vehicle Detection in Aerial Imagery (VEDAI), and 93.13% over the German Aerospace Center (DLR) DLR3K datasets, respectively. The proposed system has a variety of applications, including identifying vehicles in traffic, sensing traffic congestion on a road, traffic density at intersections, detecting various types of vehicles, and providing a path for pedestrians.
AB - Increased traffic density, combined with global population development, has resulted in increasingly congested roads, increased air pollution, and increased accidents. Globally, the overall number of automobiles has expanded dramatically during the last decade. Traffic monitoring in this environment is undoubtedly a significant difficulty in various developing countries. This work introduced a novel vehicle detection and classification system for smart traffic monitoring that uses a convolutional neural network (CNN) to segment aerial imagery. These segmented images are examined to further detect the vehicles by incorporating novel customized pyramid pooling. Then, these detected vehicles are classified into various subcategories. Finally, these vehicles are tracked via Kalman filter (KF) and kernelized filter-based techniques to cope with and manage massive traffic flows with minimal human intervention. During the experimental evaluation, our proposed system illustrated a remarkable vehicle detection rate of 95.78% over the Vehicle Aerial Imagery from a Drone (VAID), 95.18% over the Vehicle Detection in Aerial Imagery (VEDAI), and 93.13% over the German Aerospace Center (DLR) DLR3K datasets, respectively. The proposed system has a variety of applications, including identifying vehicles in traffic, sensing traffic congestion on a road, traffic density at intersections, detecting various types of vehicles, and providing a path for pedestrians.
KW - Aerial images
KW - convolutional neural network
KW - correlation filter
KW - segmentation
KW - traffic monitoring
KW - vehicles
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U2 - 10.1109/ACCESS.2023.3234281
DO - 10.1109/ACCESS.2023.3234281
M3 - Article
AN - SCOPUS:85147210933
SN - 2169-3536
VL - 11
SP - 2993
EP - 3007
JO - IEEE Access
JF - IEEE Access
ER -