Abstract
Background subtraction method is an effective moving target detection method. The difficulty lies in looking for the ideal, reliable background model for complex scenes and being updated well. While Gaussian mixture model can quickly establish a good background model, process fast, and eliminate the impact of light well. So it becomes one of the commonly used methods in target detection. This paper presents a background subtraction algorithm based on Gaussian mixture. First, background can be obtained accurately using Gaussian mixture model. Then the video lost in the process of establishing is dealt with using background subtraction method. Lastly, detect the target.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lipton AJ, Fujiyoshi H, Patil RS (eds) Moving target classification and tracking from real-time video. WACV’98 Proceedings, fourth IEEE workshop on applications of computer vision, 1998: IEEE
Collins RT, Lipton A, Kanade T, Fujiyoshi H, Duggins D, Tsin Y et al (2000) A system for video surveillance and monitoring: Carnegie Mellon University, the Robotics Institute, Pittsburg
Meyer D, Denzler J, Niemann H (eds) (1997) Model based extraction of articulated objects in image sequences for gait analysis. In: Proceedings, international conference on image processing, 1997, IEEE
Haritaoglu I, Harwood D, Davis LS (2000) W 4: real-time surveillance of people and their activities. IEEE Trans Pattern Anal Mach Intell 22(8):809–830
Toyama K, Krumm J, Brumitt B, Meyers B (eds) Wallflower: principles and practice of background maintenance. In: The proceedings of the seventh IEEE international conference on computer vision, 1999, IEEE
Karmann KP, Brandt A (eds) (1990) Moving object recognition using an adaptive background memory. In: Cappellini V (ed) Time-varying image processing and moving object recognition, vol 2. Elsevier Science Publishers B.V, Amsterdam
Kilger M (ed) (1992) A shadow handler in a video-based real-time traffic monitoring system. In: IEEE workshop on applications of computer vision, proceedings, 1992: IEEE
Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. Computer Vision—ECCV 2000. Springer, Heidelberg, pp 751–767
Stauffer C, Grimson WEL (eds) (1999) Adaptive background mixture models for real-time tracking. IEEE computer society conference on computer vision and pattern recognition, 1999, IEEE
Harville M (2002) A framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models. In: Computer vision—ECCV 2002. Springer, Heidelberg, pp 543–560
Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757
Lee D-S, Hull JJ, Erol B (eds) (2003) A Bayesian framework for Gaussian mixture background modeling. In: Proceedings 2003 international conference on image processing, 2003 ICIP 2003, IEEE
Hui Y, Songzhi S, Li W, Shaizi L (2008) Moving objects detection method based on the improved adaptive background mixture model
Wen H, Fenggang H, Han S (2005) Human gait recognition based on continuous HMM. Appl Sci Technol 32(2):50–52
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, K., Liang, Y., Xing, X., Zhang, R. (2015). Target Detection Algorithm Based on Gaussian Mixture Background Subtraction Model. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-662-46469-4_47
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46468-7
Online ISBN: 978-3-662-46469-4
eBook Packages: EngineeringEngineering (R0)