Title: A fast background model using kernel density estimation and distance transform

Authors: Jianzhao Cao; Ruwei Ma; Oloro Michael

Addresses: Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, 110168, China ' Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, 110168, China ' Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, 110168, China

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.

Keywords: image processing; background model; kernel density estimation; distance transform; people counting.

DOI: 10.1504/IJMIC.2019.102363

International Journal of Modelling, Identification and Control, 2019 Vol.32 No.2, pp.135 - 144

Received: 28 Sep 2018
Accepted: 13 Dec 2018
Published online: 23 Sep 2019 *

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