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Robust background model for pixel based people counting using a single uncalibrated camera

Choudri, S., Ferryman, J. and Badii, A. (2009) Robust background model for pixel based people counting using a single uncalibrated camera. In: Twelfth IEEE international workshop on performance evaluation of tracking and surveillance (PETS-Winter), Snowbird, Utah, USA,

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To link to this item DOI: 10.1109/PETS-WINTER.2009.5399531


Several pixel-based people counting methods have been developed over the years. Among these the product of scale-weighted pixel sums and a linear correlation coefficient is a popular people counting approach. However most approaches have paid little attention to resolving the true background and instead take all foreground pixels into account. With large crowds moving at varying speeds and with the presence of other moving objects such as vehicles this approach is prone to problems. In this paper we present a method which concentrates on determining the true-foreground, i.e. human-image pixels only. To do this we have proposed, implemented and comparatively evaluated a human detection layer to make people counting more robust in the presence of noise and lack of empty background sequences. We show the effect of combining human detection with a pixel-map based algorithm to i) count only human-classified pixels and ii) prevent foreground pixels belonging to humans from being absorbed into the background model. We evaluate the performance of this approach on the PETS 2009 dataset using various configurations of the proposed methods. Our evaluation demonstrates that the basic benchmark method we implemented can achieve an accuracy of up to 87% on sequence ¿S1.L1 13-57 View 001¿ and our proposed approach can achieve up to 82% on sequence ¿S1.L3 14-33 View 001¿ where the crowd stops and the benchmark accuracy falls to 64%.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:14633
Uncontrolled Keywords:correlation methods, image classification, object detection , empty background sequences, foreground pixels, human classified pixels, human detection, linear correlation coefficient, pixel based people counting, pixel map, robust background model, scale-weighted pixel sums, single uncalibrated camera , background modeling, crowd analysis, human detection, people counting

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