A fast background model using kernel density estimation and distance transform
by Jianzhao Cao; Ruwei Ma; Oloro Michael
International Journal of Modelling, Identification and Control (IJMIC), Vol. 32, No. 2, 2019

Abstract: Background modelling is a key factor for foreground detection, which is imperative for people counting in a dynamic environment. In this paper, a background model with improved fast kernel density estimation technique while adopting distance transform is proposed for people counting. The kernel density estimation background model is improved by early-break method and LUT. Distance transform adopted is used to separate people who are closed together as one blob. It has been tested in a 2.6 GHz Intel Core computer with 25 fps on 432 × 240 images without code optimisation. The result shows that the improved method can be used in real-time processing and has a good result.

Online publication date: Mon, 23-Sep-2019

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