A comparison of people counting techniques via video scene analysis

Poo Kuan Hoong*, Ian K. T. Tan, Chai Kai Weng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Real-time human detection and tracking from video surveillance footages is one of the most active research areas in computer vision and pattern recognition. This is due to the widespread application from being able to do it well. One such application is the counting of people, or density estimation, where the two key components are human detection and tracking. Traditional methods such as the usage of sensors are not suitable as they are not easily integrated with current video surveillance systems. As video surveillance systems are currently prevalent in most places, using vision based people counting techniques will be the logical approach. In this paper, we compared the two commonly used techniques which are Cascade Classifier and Histograms of Gradients (HOG) for human detection. We evaluated and compared these two techniques with three different video datasets with three different setting characteristics. From our experiment results, both Cascade Classifier and HOG techniques can be used for people counting to achieve moderate accuracy results.

Original languageEnglish
Pages (from-to)18123-18129
Number of pages7
JournalARPN Journal of Engineering and Applied Sciences
Volume10
Issue number23
Publication statusPublished - 2015

Keywords

  • Cascade Classifier
  • Histograms of Oriented Gradients (HOG)
  • Human tracking
  • OpenCV
  • People counting

ASJC Scopus subject areas

  • Engineering(all)

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