Abstract
Accurate segmentation of foreground objects in video scenes is critical for assuring reliable performance of vision systems for object tracking and situational awareness in outdoor scenes. Most existing techniques for background modeling and shadow suppression require that a number of parameters be “hand-tuned” based on environmental conditions. This paper presents two contributions to overcome such limitations. First, we develop and demonstrate a satellite imagery based approach for selecting appropriate background and shadow models. It is shown that the illumination conditions (i.e. cloud cover) of a scene can be reliably inferred from visible satellite images in the local region of the camera. The second contribution presented in the paper is introduction and evaluation of a Hybrid Cone-Cylinder Codebook (HC3) model which combines an adaptive efficient background model with HSV-color space shadow suppression into a single coherent framework. The structure of the HC3 model allows for seamless fusion of the satellite data. We are thereby able to exploit the fact that, for example, shadows are more pronounced on sunny days than cloudy days, allowing for more sensitive detection. The paper presents a set of experiments using day long sequences of videos from an operational surveillance system testbed. Results of these experimental analyses quantitatively illustrate the benefits of using satellite imagery to inform and adaptively adjust background and shadow modeling.
Similar content being viewed by others
References
Amnuaykanchanasin, P., Thongkamwitoon, T., Srisawaiwilai, N., Aramvith, S., Chalidabhongse, T.H.: Adaptive parametric statistical background subtraction for video segmentation. In:Proceedings of the Third ACM Int’l Workshop on Video Surveillance & Sensor Networks, pp.63–66, New York. ACM Press (2005)
Baba, M., Asada, N.: Shadow removal from a real picture. In: SIGGRAPH ’03: ACM SIGGRAPH 2003 Sketches & Applications, pp.1–1, New York. ACM Press (2003)
Cavallaro A., Salvador E. and Ebrahimi T. (2005). Shadow-aware object-based video process. IEE Visi. Image Signal Process. 152(4): 14–22
Chandrasekhar, S.: Radiative Transfer., 2nd edn. Dover Publications (1960)
Colwell, R. (ed.) : Manual of Remote Sensing, chap. 5, pp.165–230. American Society of Photogrammetry, Falls Church 2nd edn, (1983)
Cucchiara, R., Grana, C., Piccardi, M., Prati, A., Sirotti, S.: Improving shadow suppression in moving object detection with HSV color information. In: IEEE Int’l Conf. Intelligent Transportation Systems, pp.334–339 (2001)
Doshi, A., Trivedi, M.M.: Hybrid cone-cylinder codebook model for foreground detection with shadow and highlight suppression. In: IEE Int’l Conf. on Advanced Video and Signal based Surveillance. Australia (2006)
Elgammal, A.M., Harwood, D., Davis, L.S.: Non-parametric model for background subtraction. In: European Conf. on Computer Vision, pp.751–767 (2000)
Han, H., Wang, Z., Liu, J., Li, Z., Li, B., Han, Z.: Adaptive background modeling with shadow suppression. In: Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE, vol. 1, pp.720–724 (2003)
Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for realtime robust background subtraction and shadow detection. In: Proceedings of IEEE Frame Rate Workshop, pp.1–19 (1999)
Joseph J.H., Wiscombe W. and Weinman J. (1976). The delta-eddington approximation for radiative flux transfer. J. Atmos. Sci. 33(12): 2452–2459
Kim K., Chalidabhonse T.H., Harwood D. and Davis L. (2005). Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11(3): 167–256
Lee D.-S., Hull J.J. and Erol B. (2003). A bayesian framework for gaussian mixture background modeling. IEEE Int. Conf. Image Process. 3: 973–976
Liou K.-N.: Radiation and Cloud Processes in the Atmosphere: Theory, Observation, and Modeling. Oxford University Press (1992)
Liou K.-N. and Wittman G.D. (1979). Parameterization of the radiative properties of clouds. J. Atmos. Sci. 36(7): 1261–1273
Lluis, J., Miralles, X., Bastidas, O.: Reliable real-time foreground detection for video surveillance applications. In: Proceedings of the Third ACM Int’l Workshop on Video Surveillance & Sensor Networks, pp.59–62, New York. ACM Press (2005)
Meador W. and Weaver W. (1980). Two-stream approximations to radiative transfer in planetary atmospheres: A unified description of existing methods and a new improvement. J. Atmos. Sci. 37(3): 630–643
Mikic, I., Kogut, G., Cosman, P., Trivedi, M.M.: Moving shadow and object detection in traffic scenes. Int. Conf. on Pattern Recogn. (2000)
NASA: Clouds and radiation. Earth observatory: http://www.earthobservatory.nasa.gov/Library/Clouds/
GOES I-M Databook rev. 1, Space Systems-Loral. NASA, http://www.rsd.gsfc.nasa.gov/goes/text/goes.databook.html. Chapter on Imager, pp. 20–38 (2001)
NOAA: Current GOES-10 1 km VIS Satellite Imagery Centered on San Diego. http://www.sat.wrh.noaa.gov/satellite/1km/Sandiego/VIS1SAN.GIF
NOAA: Geostationary Satellite Server. http://www.goes.noaa.gov/
Piccardi M. (2004). Background subtraction techniques: a review. IEEE Int. Conf. Systems Man Cybern. 4: 3099–3104
Porikli, F., Thornton, J.: Shadow flow: A recursive method to learn moving cast shadows. In: IEEE International Conference on Computer Vision (ICCV) (2005)
Porikli, F., Tuzel O.: Bayesian background modeling for foreground detection. In:Proceedings of the Third ACM Int’l Workshop on Video Surveillance & Sensor Networks, pp.55–58, New York. ACM Press (2005)
Prati, A., Mikic, I., Cucchiara, R., Trivedi, M. M.: Analysis and detetcion of shadows in video streams: a comparative evaluation. In: IEEE CVPR Workshop on Empirical Evaluation Methods in Computer Vision (2001)
Prati A., Mikic I., Trivedi M. and Cucchiara R. (2003). Detecting moving shadows: Algorithms and evaluation. IEEE Trans. Pattern Anal. Mach. Intelli. 25: 918–923
Rossow W.B. (1989). Measuring cloud properties from space: a review. J. Climate 2(3): 201–213
Stauder J., Mech R. and Ostermann J. (1999). Detection of moving cast shadows for object segmentation. IEEE Trans. Multimedia 1(1): 65–76
Stauffer C. and Grimson W.L. (1999). Adaptive background mixture models for real-time tracking. CVPR99 II: 246–252
Tattersall, S., Dawson-Howee, K.: Adaptive shadow identification through automatic parameter estimation in video sequences. In: Irish Machine Vision and Image Processing Conference (IMVIP 2003), pp.57–64 (2003)
Toyama, K., Krumm, J., Brumitt, B. Meyers, B.: Wallflower: principles and practice of background maintenance. In:ICCV (1), pp.255–261 (1999)
Trivedi, M.M.: Analysis of high-resolution aerial images. In: Kasturi, R., Trivedi, M.M. (eds.) Image Analysis Applications, pp.281–305. Marcel Dekker (1990)
Trivedi M.M., Gandhi T.L. and Huang K.S. (2005). Distributed interactive video arrays for event capture and enhanced situational awareness. IEEE Intell. Systems 20(5): 58–66
Wren C.R., Azarbayejani A., Darrell T. and Pentland A. (1997). Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7): 780–785
Zhu X. and Arking A. (1994). Comparison of daily averaged reflection, transmission and absorption for selected radiative flux transfer approximations. J. Atmos. Sci. 51(24): 3580–3592
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Doshi, A., Trivedi, M.M. Satellite imagery based adaptive background models and shadow suppression. SIViP 1, 119–132 (2007). https://doi.org/10.1007/s11760-007-0013-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-007-0013-8