Smart Traffic Monitoring Through Pyramid Pooling Vehicle Detection and Filter-Based Tracking on Aerial Images

Abstract

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.

Improved Deep Learning-Based Vehicle Detection for Urban Applications Using Remote Sensing Imagery

“…In [12], the authors developed a backbone architecture with a context data component and attention module to detect vehicles from a real image, which allowed the extraction feature network to improve the deployment of context data and prominent areas. Rafique et al [13] presented a new vehicle recognition and segmentation technique for the monitoring of smart traffic, which employed a CNN for real image classification. Later, the identified vehicles were grouped into distinct subgroups.…”

CNN with New Spatial Pyramid Pooling and Advanced Filter-Based Techniques: Revolutionizing Traffic Monitoring via Aerial Images

“…Recurrent-Feature Aware Visualization (RFAV) 56.24 [40] CNN based on Tectofugal-thalamofugal-accessory optic system 84.25 [41] Customized Pyramid Pooling Method (CPPM) 93.13 [42] Proposed Model 93.8…”

“…Recurrent-Feature Aware Visualization (RFAV) 51.95 [40] Customized Pyramid Pooling Method (CPPM) 92.22 [42] Feature-balanced pyramid network (FBPN) 91.27 [43] Proposed Model 95.4…”

Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences

“…In recent years, vehicle detection and classification has been an emerging research area due to its various applications in intelligent traffic management systems. Road Traffic management applications include congestion detection, categorizing the various vehicle types, recognizing doubtful vehicles on the road, and parking management system [1]. All these systems mainly depend on vehicle identification, which has become a significant and crucial issue in aerial imagery [2].…”