Abstract
This paper proposes an optimisation of the adaptive Gaussian mixture background model that allows the deployment of the method on processors with low memory capacity. The effect of the granularity of the Gaussian mean-value and variance in an integer-based implementation is investigated and novel updating rules of the mixture weights are described. Based on the proposed framework, an implementation for a very low power consumption micro-controller is presented. Results show that the proposed method operates in real time on the micro-controller and has similar performance to the original model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004)
Cheung, S.C.S., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Visual Communications and Image Processing, vol. 5308, pp. 881–892 (2004)
Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: A systematic survey. IEEE Transactions on Image Processing 14, 294–307 (2005)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2246–2252 (1999)
Microchip Technology Inc.: PIC32MX3XX/4XX Family Data Sheet (2008), ww1.microchip.com/downloads/en/DeviceDoc/61143E.pdf
McFarlane, N.J.B., Schofield, C.P.: Segmentation and tracking of piglets in images. Machine Vision and Applications 8, 187–193 (1995)
Hung, M.H., Pan, J.S., Hsieh, C.H.: Speed up temporal median filter for background subtraction. In: Proceedings of International Conference on Pervasive Computing, Signal Processing and Applications, pp. 297–300 (2010)
Rahimi, M., Baer, R., Iroezi, O.I., Garcia, J.C., Warrior, J., Estrin, D., Srivastava, M.: Cyclops: In situ image sensing and interpretation in wireless sensor networks. In: Proceedings of ACM Conference on Embedded Networked Sensor Systems, pp. 192–204 (2005)
Iannizzotto, G., La Rosa, F., Lo Bello, L.: A wireless sensor network for distributed autonomous traffic monitoring. In: Conference on Human System Interactions, pp. 612–619 (2010)
IEEE Computer Society: IEEE Standard for Floating-Point Arithmetic. Technical report, Microprocessor Standards Committee of the IEEE Computer Society, 3 Park Avenue, New York, NY 10016-5997, USA (2008)
Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding 104, 249–257 (2006)
Tan, B., Zhang, J., Wang, L.: Semi-supervised elastic net for pedestrian counting. Pattern Recognition (2011)
Scuola Superiore Sant’Anna and Evidence s.r.l.: SEED-EYE BOARD (2011), http://www.evidence.eu.com/products/seed-eye.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salvadori, C., Makris, D., Petracca, M., Martinez-del-Rincon, J., Velastin, S. (2012). Gaussian Mixture Background Modelling Optimisation for Micro-controllers. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-33179-4_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33178-7
Online ISBN: 978-3-642-33179-4
eBook Packages: Computer ScienceComputer Science (R0)