An Adaptive Moving Objects Detection Algorithm Based on Kernel Density Estimation
Year: 2013
Type: article
Abstract: The detection of moving objects are important research area for video surveillance and other video processing applications. In this paper, we propose an adaptive approach modeling background and segmenting moving object with non-parametric kernel density estimation. Unlike previous approaches to object detection which detect objects by global threshold, we use a local threshold to reflect temporal persistence. With combined of global threshold and local thresholds, the proposed approach can handle scenes containing gradual illumination variations and noise and has no bootstrapping limitations. Experimental results on different types of videos demonstrate the utility and performance of the proposed approach.
Source: Applied Mechanics and Materials
Authors: Man Hua, Yan Ling Li, Rui Chun Lin
Institution: Civil Aviation Flight University of China
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
OpenAlex ID: W1994557168