Vision-based estimation of the number of occupants using video cameras

Ipek Gursel Dino, Esat Kalfaoglu, Orcun Koral Iseri, Bilge Erdogan, Sinan Kalkan, A. Aydin Alatan

Research output: Contribution to journalArticlepeer-review

Abstract

Although occupancy information is critical to energy consumption of existing buildings, it still remains to be a major source of uncertainty. For reliable and accurate occupant modeling with minimal uncertainties, capturing precise occupant information on occupants is essential. This paper proposes a computer vision-based approach that utilizes deep learning architectures to estimate of the number of people in large, crowded spaces using multiple cameras. Various vision techniques (head detection, background elimination, head tracking) are implemented in three methods: (i) a method that instantaneously counts people in a scene, (ii) a method that incrementally counts people entering/exiting a room and (iii) a combination of the first two methods. These methods were applied in a classroom with heavy occlusions, and resulted in a high prediction capacity when compared to ground truth measurements. Future work in video-analytical approaches can address problems regarding lowering the computational cost of analysis, capturing occupancy data in complex room geometries and addressing concerns in privacy preservation.
Original languageEnglish
Article number101662
JournalAdvanced Engineering Informatics
Volume53
Early online date7 Jun 2022
DOIs
Publication statusPublished - Aug 2022

Keywords

  • Building occupancy
  • Computer vision
  • Deep learning
  • Video content analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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