Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31103
A Background Subtraction Based Moving Object Detection around the Host Vehicle

Authors: Hyojin Lim, Cuong Nguyen Khac, Ho-Youl Jung


In this paper, we propose moving object detection method which is helpful for driver to safely take his/her car out of parking lot. When moving objects such as motorbikes, pedestrians, the other cars and some obstacles are detected at the rear-side of host vehicle, the proposed algorithm can provide to driver warning. We assume that the host vehicle is just before departure. Gaussian Mixture Model (GMM) based background subtraction is basically applied. Pre-processing such as smoothing and post-processing as morphological filtering are added. We examine “which color space has better performance for detection of moving objects?” Three color spaces including RGB, YCbCr, and Y are applied and compared, in terms of detection rate. Through simulation, we prove that RGB space is more suitable for moving object detection based on background subtraction.

Keywords: Gaussian mixture model, background subtraction, moving object detection, color space, morphological filtering

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2297


[1] Zivkovic, Zoran. "Improved adaptive Gaussian mixture model for background subtraction." Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. Vol. 2. pp. 28-31, IEEE, 2004.
[2] Schick, Alexander, M. Bauml, and Rainer Stiefelhagen. "Improving foreground segmentations with probabilistic super pixel markov random fields." Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on. pp. 27-31, IEEE, 2012
[3] Zhou, Dongxiang, and Hong Zhang. "Modified GMM background modeling and optical flow for detection of moving objects." Systems, Man and Cybernetics, 2005 IEEE International Conference on. Vol. 3. pp. 2224-2229, IEEE, 2005
[4] Lim, Jongwoo, and Bohyung Han. "Generalized Background Subtraction Using Superpixels with Label Integrated Motion Estimation." Computer Vision–ECCV 2014. Springer International Publishing, pp, 173-187, 2014.
[5] Kwak, Suha, et al. "Generalized background subtraction based on hybrid inference by belief propagation and bayesian filtering." Computer Vision (ICCV), 2011 IEEE International Conference on. pp. 2174-2181, IEEE, 2011.