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, Moatasem Al-Gunaid

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Article number306
JournalFuture Internet
Issue number12
Publication statusPublished - Dec 2021


  • Correlation filters
  • Deep learning
  • Multi-object tracking
  • Object counting
  • Object detection
  • YOLO

ASJC Scopus subject areas

  • Computer Networks and Communications


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