A Background Subtraction Based Moving Object Detection around the Host Vehicle
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:70394", author = "Hyojin Lim and Cuong Nguyen Khac and Ho-Youl Jung", title = "A Background Subtraction Based Moving Object Detection around the Host Vehicle", abstract = "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.", volume = "9", number = "8", pages = "1867-4", }