An adaptive people counting system with dynamic features selection and occlusion handling

Zeyad Q. H. Al-Zaydi, David Ndzi, Yanyan Yang, Munirah L. Kamarudin

Research output: Contribution to journalArticle

7 Citations (Scopus)
11 Downloads (Pure)

Abstract

This paper presents an adaptive crowd counting system for video surveillance applications. The proposed method is composed of a pair of collaborative Gaussian process models (GP) with different kernels, which are designed to count people by taking the level of occlusion into account. The level of occlusion is measured and compared with a predefined threshold for regression model selection for each frame. In addition, the proposed method dynamically identifies the best combination of features for people counting. The Mall and UCSD datasets are used to evaluate the proposed method. The results show that the proposed method offers a higher accuracy when compared against state of the art methods reported in open literature. The mean absolute error (MAE), mean squared error (MSE) and the mean deviation error (MDE) for the proposed algorithm are 2.90, 13.70 and 0.095, respectively, for the Mall dataset and 1.63, 4.32 and 0.066, respectively, for UCSD dataset.
Original languageEnglish
Pages (from-to)218-225
Number of pages8
JournalJournal of Visual Communication and Image Representation
Volume39
Early online date3 Jun 2016
DOIs
Publication statusPublished - 1 Aug 2016
Externally publishedYes

Fingerprint

Feature extraction
Shopping centers

Keywords

  • Crowd counting
  • Surveillance systems
  • Image processing
  • Computer vision

Cite this

@article{30d0f4ba814a493d8f2880b9a6f843c3,
title = "An adaptive people counting system with dynamic features selection and occlusion handling",
abstract = "This paper presents an adaptive crowd counting system for video surveillance applications. The proposed method is composed of a pair of collaborative Gaussian process models (GP) with different kernels, which are designed to count people by taking the level of occlusion into account. The level of occlusion is measured and compared with a predefined threshold for regression model selection for each frame. In addition, the proposed method dynamically identifies the best combination of features for people counting. The Mall and UCSD datasets are used to evaluate the proposed method. The results show that the proposed method offers a higher accuracy when compared against state of the art methods reported in open literature. The mean absolute error (MAE), mean squared error (MSE) and the mean deviation error (MDE) for the proposed algorithm are 2.90, 13.70 and 0.095, respectively, for the Mall dataset and 1.63, 4.32 and 0.066, respectively, for UCSD dataset.",
keywords = "Crowd counting, Surveillance systems, Image processing, Computer vision",
author = "Al-Zaydi, {Zeyad Q. H.} and David Ndzi and Yanyan Yang and Kamarudin, {Munirah L.}",
year = "2016",
month = "8",
day = "1",
doi = "10.1016/j.jvcir.2016.05.018",
language = "English",
volume = "39",
pages = "218--225",
journal = "Journal of Visual Communication and Image Representation",
issn = "1047-3203",
publisher = "Elsevier B.V.",

}

An adaptive people counting system with dynamic features selection and occlusion handling. / Al-Zaydi, Zeyad Q. H. ; Ndzi, David; Yang, Yanyan; Kamarudin, Munirah L.

In: Journal of Visual Communication and Image Representation, Vol. 39, 01.08.2016, p. 218-225.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An adaptive people counting system with dynamic features selection and occlusion handling

AU - Al-Zaydi, Zeyad Q. H.

AU - Ndzi, David

AU - Yang, Yanyan

AU - Kamarudin, Munirah L.

PY - 2016/8/1

Y1 - 2016/8/1

N2 - This paper presents an adaptive crowd counting system for video surveillance applications. The proposed method is composed of a pair of collaborative Gaussian process models (GP) with different kernels, which are designed to count people by taking the level of occlusion into account. The level of occlusion is measured and compared with a predefined threshold for regression model selection for each frame. In addition, the proposed method dynamically identifies the best combination of features for people counting. The Mall and UCSD datasets are used to evaluate the proposed method. The results show that the proposed method offers a higher accuracy when compared against state of the art methods reported in open literature. The mean absolute error (MAE), mean squared error (MSE) and the mean deviation error (MDE) for the proposed algorithm are 2.90, 13.70 and 0.095, respectively, for the Mall dataset and 1.63, 4.32 and 0.066, respectively, for UCSD dataset.

AB - This paper presents an adaptive crowd counting system for video surveillance applications. The proposed method is composed of a pair of collaborative Gaussian process models (GP) with different kernels, which are designed to count people by taking the level of occlusion into account. The level of occlusion is measured and compared with a predefined threshold for regression model selection for each frame. In addition, the proposed method dynamically identifies the best combination of features for people counting. The Mall and UCSD datasets are used to evaluate the proposed method. The results show that the proposed method offers a higher accuracy when compared against state of the art methods reported in open literature. The mean absolute error (MAE), mean squared error (MSE) and the mean deviation error (MDE) for the proposed algorithm are 2.90, 13.70 and 0.095, respectively, for the Mall dataset and 1.63, 4.32 and 0.066, respectively, for UCSD dataset.

KW - Crowd counting

KW - Surveillance systems

KW - Image processing

KW - Computer vision

U2 - 10.1016/j.jvcir.2016.05.018

DO - 10.1016/j.jvcir.2016.05.018

M3 - Article

VL - 39

SP - 218

EP - 225

JO - Journal of Visual Communication and Image Representation

JF - Journal of Visual Communication and Image Representation

SN - 1047-3203

ER -