Background Subtraction Based on Pulse Coupled Neural Network

Abstract:

Dynamic environments often bring great challenges to moving object detection. Solving this problem will promote expansion of application fields of moving object detection. Unlike those background subtraction methods using local feature-based background models, inspired by integrity of human visual perception, we present a background subtraction method for moving object detection in dynamic environments, building its background models based on global features extracted by pulse coupled neural network. We employ the pulse coupled neural network to take good advantage of their global coupling characters, in order to imitate the human biological visual activity. After sensing images via the pulse coupled neural network, we extract global information of the scenes and then build background models robust to background disturbances based on the global features. Experimental results prove that, our method behaves well in terms of visual and quantitative evaluations for dynamic environments.

• Periodical: Applied Mechanics and Materials (Volumes 701-702)
• Pages: 293-296
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.701-702.293
• Online since: December 2014
• Authors: Xi Cai*, Guang Han, Jin Kuan Wang
• Keywords: Background Subtraction, Integrity of Human Visual Perception, Pulse Coupled Neural Network

References

[1] T. Bouwmans: Traditional and recent approaches in background modeling for foreground detection: An overview, Computer Science Review Vol. 11-12 (2014), pp.31-66.

DOI: 10.1016/j.cosrev.2014.04.001

[2] C. Stauffer and W. Grimson: Learning patterns of activity using real-time tracking, IEEE Trans. Pattern Anal. Mach. Intell. Vol. 22 (2000), p.747–757.

DOI: 10.1109/34.868677

[3] Z. Zivkovic and F. V. D Heijden: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognit. Lett. Vol. 27 (2006), p.773–780.

DOI: 10.1016/j.patrec.2005.11.005

[4] M. Heikkila and M. Pietikainen: A texture-based method for modeling the background and detecting moving objects, IEEE Trans. Pattern Anal. Mach. Intell. Vol. 28 (2006), p.657–662.

DOI: 10.1109/tpami.2006.68

[5] C. Yeh, C. Lin, K. Muchtar and L. Kang: Real-time background modeling based on a multi-level texture description, Information Sciences Vol. 269 (2014), pp.106-127.

DOI: 10.1016/j.ins.2013.08.014

[6] B. Han and L. S. Davis: Density-based multifeature background subtraction with Support Vector Machine, IEEE Trans. Pattern Anal. Mach. Intell. Vol. 34 (2012), p.1017–1023.

DOI: 10.1109/tpami.2011.243

[7] Z. Wang, Y. Ma, F. Cheng and L. Yang: Review of pulse-coupled neural networks, Image Vis. Comput. Vol. 28 (2010), p.5–13.

[8] S. Brutzer, B. Hoferlin and G. Heidemann: Evaluation of background subtraction techniques for video surveillance, in IEEE Conference on Computer Vision and Pattern Recognition (2011), p.1937–(1944).

DOI: 10.1109/cvpr.2011.5995508