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

Adnan Ahmed Rafique, Amal Al-Rasheed, Amel Ksibi, Manel Ayadi, Ahmad Jalal, Khaled Alnowaiser, Hossam Meshref, Mohammad Shorfuzzaman, Munkhjargal Gochoo, Jeongmin Park

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2993-3007
Number of pages15
JournalIEEE Access
Publication statusPublished - 2023


  • Aerial images
  • convolutional neural network
  • correlation filter
  • segmentation
  • traffic monitoring
  • vehicles

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering


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