Abstract
A Multi-phase visible-infrared image fusion algorithm was proposed for bi-channel motion object detection. First of all, foreground detected separately by visible and infrared image was fused as foreground-fused image, and then an improved KIRSCH algorithm was used to calculate the complete contour from foreground-fused image, taking advantage of the complementary characteristics of the visible and infrared images by use of a fused image provided by channel-replacement-operation. At last, a complete moving target was obtained with the holes fill technology .Experimental results show that the proposed algorithm can effectively remove the shadow of the foreground in visible image, and access to the clear and complete moving target.
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
Haritaoglu, I., Harwood, D., Davis, L.: W4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)
Lipton, A., Fujiyoshi, H., Patil, R.: Moving target classification and tracking from real-time video. In: IEEE Workshop on Applications of Computer Vision, Princeton, USA, pp. 8–14 (1998)
Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 42–47 (1994)
Pohl, C., Genderen, J.L.: Multisensor Image Fusion in Remote Sensing Concepts, Methods and Applications. International Journal of Remote Sensing 9(5), 823–854 (1998)
Zhang, X.-W., Zhang, Y.-N.: Advances and perspective on motion detection fusion in visual and thermal framework. J. Infrared Millim. Waves 30(4) (August 2011)
Verstockt, S., Poppe, C., De Potter, P.: Silhouette Coverage Analysis for Multi-modal Video Surveillance. In: Progress In Electromagnetics Research Symposium Proceedings, Marrakesh, Morocco, March 20-23, vol. 1279 (2011)
OTCBVS Benchmark Dataset Collection, http://www.cse.ohio-state.edu/otcbvs-bench/
Ulusoy, H., Yuruk, H.: New method for fusion of complementary information from infrared and visual images for object detection. IET Image Processing 5(1), 36–48 (2011)
Zhang, L., Wu, B., Nevatia, R.: Pedstrian detection in infrared images based on local shape fetures. In: Fourth Joint IEEE Int. Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum (OTCBVS 2007), in Conjunction with CVPR (2007)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Part vol. 2. IEEE Comput. Soc (1999)
Schnelle, S.R., Chan, A.L.: Enhanced Target Tracking Through Infrared-Visible Image Fusion. IEEE (2011)
Liu, Z., et al.: Object Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(1) (January 2012)
Zhang, D.-C., Zhou, C.-G.: Hole-Filling Algorithm Based on Contour. Journal of Jilin University (Science Edition)Â 49(1) (January 2011)
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
Chen, Y., Wang, ZM., Bao, H. (2012). Multi-phase Fusion of Visible-Infrared Information for Motion Detection. In: Wang, F.L., Lei, J., Lau, R.W.H., Zhang, J. (eds) Multimedia and Signal Processing. CMSP 2012. Communications in Computer and Information Science, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35286-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-35286-7_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35285-0
Online ISBN: 978-3-642-35286-7
eBook Packages: Computer ScienceComputer Science (R0)