Time-Derivative Estimation of Noisy Movie Data using Adaptive Control Theory
This paper presents an adaptive differentiator
of sequential data based on the adaptive control theory. The
algorithm is applied to detect moving objects by estimating a
temporal gradient of sequential data at a specified pixel. We
adopt two nonlinear intensity functions to reduce the influence
of noises. The derivatives of the nonlinear intensity functions
are estimated by an adaptive observer with σ-modification
update law.
[1] Cucchiara,R., Grana,C., Piccardi,M., and Prati,A. : "Detecting
Moving Objects, Ghosts, and Shadows in Video Streams", IEEE
Trans. on PAMI, Vol.25, No.10, pp.1337-1342 (2003).
[2] Cheung,S.-C. and Kamath,C. : "Robust techniques for background
subtraction in urban traffic video", Video Communications
and Image Processing, SPIE Electronic Imaging, San Jose,
January (2004), UCRL-CONF-200706.
[3] Manzanera,A. and Richefeu,J.C. : "A robust and computationally
efficient motion detection algorithm based on Σ − Δ background estimation", ICVGIP, Kolkata, India (2004).
http://www.ensta.fr/˜manzaner
[4] Halevy,G. and Weinshall,D. : "Motion of disturbances: detection
and tracking of multibody non-rigid motion",Machine
Vision and Applications, Vol.11, pp.122-137 (1999).
[5] Ibrir,S.: "New differentiators for control and observation applications",
Proc. of American Control Conference, pp.2522-2527
(2001).
[6] Ibrir,S.: "Linear time-derivative trackers", Automatica, Vol.40,
pp.397-405 (2004).
[7] Wren,C., Azabayejani,A., Darrell,T. and Pentland,A.: "Pfinder:
Real-time tracking of the human body", IEEE Trans. on PAMI,
Vol.19, No.7, pp.780-785 (1997).
[8] Kuo,C.M., Hsieh,C.-H., Lin,H.-C., and Lu, P.-C.: "Motion
estimation algorithm with Kalman filter", Electronics Letters,
Vol.30, No.15, pp.1204-1206 (1994).
[9] Karmann,K.-P. and Brandt A.: "Moving object recognition
using an adaptive background memory", Time-Varying Image
Processing and Moving Object Recognition, V.Cappellini ed.,
2, pp.289-307, Elsevier Sicence Publishers B.V. (1990).
[10] K¨oker,R., Cakar,,S., and O¨ z,C.: "Moving object detection and
target prediction in video image", IJCI Proceedings of International
Conference on Signal Processing, Vol.1, No.2, pp.149-
152 (2003).
[11] Richefeu,J. and Manzanera,A. : "A new hybrid differential
filter for motion detection", ICCVG-04, Warsaw, Poland,
22-24, Sept. (2004).
http://www.ensta.fr/˜richefeu/Publications/
iccvg82.pdf
[12] Narendra,K.S. and Annaswamy,A.M. : Stable Adaptive Systems,
Prentice-Hall, Inc. (1989).
[13] Ioannou,P.A. and Sun,J. : Robust Adaptive Control, Prentice-
Hall, Inc. (1996).
[14] Marbled-Block (new version) marmor stat, Institut
f¨ur Algorithmen und Kognitive Systeme (Group
Prof. Dr. H.-H. Nagel), Universit¨at Karlsruhe,
http://i21www.ira.uka.de/image_sequences/.
[15] Shimai,H. , Kurita,T. , and Umeyama,S. : "Adaptive Background
Estimation by Robust Statistics", IEICE Transaction,
D-II, Vol.J86-D-II, No.6, pp.796-806 (2003) (in Japanese).
[16] Shimai,H., Mishima,T., Kurita,T., and Umeyama,S. : "Adaptive
background estimation from image sequence by on-line Mestimation
and its application to detection of moving objects",
Proc. of Infotech Oulu Workshop on Real-Time Image Sequence
Analysis, pp.99-108 (2000).
[1] Cucchiara,R., Grana,C., Piccardi,M., and Prati,A. : "Detecting
Moving Objects, Ghosts, and Shadows in Video Streams", IEEE
Trans. on PAMI, Vol.25, No.10, pp.1337-1342 (2003).
[2] Cheung,S.-C. and Kamath,C. : "Robust techniques for background
subtraction in urban traffic video", Video Communications
and Image Processing, SPIE Electronic Imaging, San Jose,
January (2004), UCRL-CONF-200706.
[3] Manzanera,A. and Richefeu,J.C. : "A robust and computationally
efficient motion detection algorithm based on Σ − Δ background estimation", ICVGIP, Kolkata, India (2004).
http://www.ensta.fr/˜manzaner
[4] Halevy,G. and Weinshall,D. : "Motion of disturbances: detection
and tracking of multibody non-rigid motion",Machine
Vision and Applications, Vol.11, pp.122-137 (1999).
[5] Ibrir,S.: "New differentiators for control and observation applications",
Proc. of American Control Conference, pp.2522-2527
(2001).
[6] Ibrir,S.: "Linear time-derivative trackers", Automatica, Vol.40,
pp.397-405 (2004).
[7] Wren,C., Azabayejani,A., Darrell,T. and Pentland,A.: "Pfinder:
Real-time tracking of the human body", IEEE Trans. on PAMI,
Vol.19, No.7, pp.780-785 (1997).
[8] Kuo,C.M., Hsieh,C.-H., Lin,H.-C., and Lu, P.-C.: "Motion
estimation algorithm with Kalman filter", Electronics Letters,
Vol.30, No.15, pp.1204-1206 (1994).
[9] Karmann,K.-P. and Brandt A.: "Moving object recognition
using an adaptive background memory", Time-Varying Image
Processing and Moving Object Recognition, V.Cappellini ed.,
2, pp.289-307, Elsevier Sicence Publishers B.V. (1990).
[10] K¨oker,R., Cakar,,S., and O¨ z,C.: "Moving object detection and
target prediction in video image", IJCI Proceedings of International
Conference on Signal Processing, Vol.1, No.2, pp.149-
152 (2003).
[11] Richefeu,J. and Manzanera,A. : "A new hybrid differential
filter for motion detection", ICCVG-04, Warsaw, Poland,
22-24, Sept. (2004).
http://www.ensta.fr/˜richefeu/Publications/
iccvg82.pdf
[12] Narendra,K.S. and Annaswamy,A.M. : Stable Adaptive Systems,
Prentice-Hall, Inc. (1989).
[13] Ioannou,P.A. and Sun,J. : Robust Adaptive Control, Prentice-
Hall, Inc. (1996).
[14] Marbled-Block (new version) marmor stat, Institut
f¨ur Algorithmen und Kognitive Systeme (Group
Prof. Dr. H.-H. Nagel), Universit¨at Karlsruhe,
http://i21www.ira.uka.de/image_sequences/.
[15] Shimai,H. , Kurita,T. , and Umeyama,S. : "Adaptive Background
Estimation by Robust Statistics", IEICE Transaction,
D-II, Vol.J86-D-II, No.6, pp.796-806 (2003) (in Japanese).
[16] Shimai,H., Mishima,T., Kurita,T., and Umeyama,S. : "Adaptive
background estimation from image sequence by on-line Mestimation
and its application to detection of moving objects",
Proc. of Infotech Oulu Workshop on Real-Time Image Sequence
Analysis, pp.99-108 (2000).
@article{"International Journal of Information, Control and Computer Sciences:59473", author = "Soon-Hyun Park and Takami Matsuo", title = "Time-Derivative Estimation of Noisy Movie Data using Adaptive Control Theory", abstract = "This paper presents an adaptive differentiator
of sequential data based on the adaptive control theory. The
algorithm is applied to detect moving objects by estimating a
temporal gradient of sequential data at a specified pixel. We
adopt two nonlinear intensity functions to reduce the influence
of noises. The derivatives of the nonlinear intensity functions
are estimated by an adaptive observer with σ-modification
update law.", keywords = "Adaptive estimation, parameter adjustmentlaw, motion detection, temporal gradient, differential filter.", volume = "2", number = "8", pages = "2758-8", }