Abstract
Moving object detection is a challenging task for night security because of bad video quality. In this paper, we propose a robust real time objects detection method for night visual surveillance based on human visual system. By measuring contrast information variation in multiple successive frames, a spatio-temporal contrast change image (CCI) is formed. Then the multi-frame correspondence technology is employed to robustly extract salient motions or moving objects from CCI. Since CCI is a statistical measurement of variation based on human visual system, the proposed method is effective at night and better than traditional detection methods. Experiments on real scene show that the method based on contrast feature is effective for night object detection and tracking, our approach is also robust to camera scale variation as well as low computation cost.
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Crandall, D., Luo, J.: Robust color object detection using spatial-color joint probability functions. CVPR, 379–385 (2004)
Harris, C.G., Stephens, M.: A combined corner and edge detector. In: 4th Alvey Vision Conference, pp. 147–151 (1988)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. CVPR, 252–260 (1999)
Wren, C.R., Azarbayejani, A., Pentland, A.: Pfinder: realtime tracking of the human body. PAMI, 780–785 (1997)
Liyuan, L., Huang, W., et al.: Statistical modeling of complex backgrounds for fore-ground object detection. IEEE Trans. on Image Processing 13(11), 1459–1472 (2004)
Jain, A.K., Zhong, Y., Lakshmanan, S.: Object matching using deformable templates. PAMI 8(3), 267–278 (1996)
Sclaroff, S., Liu, L.: Deformable shape detection and description via model based region grouping. PAMI, 474–489 (2001)
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Davies, D., Palmer, P.L., Mirmehdi, M.: Detection and tracking of very small low contrast objects. BMVC, 599–608 (1998)
Tang, J., Kim, J.H., Peli, E.: Image enhancement in the JPEG domain for people with vision impairment. IEEE Transactions on Biomedical Engineering 51(11), 2013–2023 (2004)
Peli, E.: Contrast sensitivity function and image discrimination. J. Opt. Soc. Am. A 18(2), 283–293 (2001)
Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. 1, pp. 28–33. Addison-Wesley, Reading (1992)
Barten, P.G.J.: Contrast sensitivity of the Human Eye and Its Effects on Image Quality. In: SPIE, Bellingham, Washington (1999)
Gould, K., Rangarajan, K., Shah, M.: Detection and representation of events in motion trajectories. In: Gonzalez, Mahdavieh (eds.) Advances in Image Processing and Analysis. Optical Engineering Press (1992)
Reinhard, E., Shirley, P., Ashikhmin, M., Troscianko, T.: Second order mage statistics for computer graphics. In: ACM Symposium on Applied Perception in Computer Graphics and Visualization (August 2004)
Cavallaro, A., Steiger, O., Ebrahimi, T.: Multiple video object tracking in complex scenes. In: Proc. of ACM Multimedia, Juan les Pins, France, pp. 1–6 (2002)
Tamura, H., Mori, S., Yamawaki, T.: Texture features corresponding to visual perception. IEEE Trans. on Syst., Man, and Cybern. 6(4), 460–473 (1979)
Sneath, P., Sokal, R.: Numerical Taxonomy. The principle and practice of numerical classification. W.H.Freeman, New York (1973)
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© 2006 Springer-Verlag Berlin Heidelberg
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Huang, K., Wang, L., Tan, T. (2006). Detecting and Tracking Distant Objects at Night Based on Human Visual System. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_82
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DOI: https://doi.org/10.1007/11612704_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
Online ISBN: 978-3-540-32432-4
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