Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

Adaptive Object Segmentation from Surveillance Video Sequences

© 2010 by IJCA Journal
Number 1 - Article 3
Year of Publication: 2010
Murali S
Girisha R

Murali S and Girisha R. Article:Adaptive Object Segmentation from Surveillance Video Sequences. IJCA,Special Issue on RTIPPR (1):37–47, 2010. Published By Foundation of Computer Science. BibTeX

	author = {Murali S and Girisha R},
	title = {Article:Adaptive Object Segmentation from Surveillance Video Sequences},
	journal = {IJCA,Special Issue on RTIPPR},
	year = {2010},
	number = {1},
	pages = {37--47},
	note = {Published By Foundation of Computer Science}


Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications. We develop an efficient adaptive object segmentation algorithm for color video surveillance sequences; background is modeled using Multiple Correlation Coefficient (R_(a.bc)) using pixel-level based approach. Segmented foreground generally includes self shadows as foreground objects since the shadow intensity differs and gradually changes from the background in a video sequence. Moreover, self shadows are vague in nature and have no clear boundaries. To eliminate such shadows from motion segmented video sequences, we propose an algorithm based on inferential statistical Difference in Mean (Z) method. Self shadow eliminated foreground contains cast shadows. Where, cast shadows produce troublesome effects for video surveillance systems, typically for object tracking from a fixed viewpoint. It yields appearance variations of objects depending on whether they are inside or outside the shadows. To eliminate cast shadows from video sequences, we propose an algorithm based on the fact that, cast shadow points are usually adjacent to object points and are merged in a single blob on the edge of the moving objects. Also cast shadow occurs only at run time (as objects move in the scene). The approach uses the Standard Scores (S) to build statistical model. This statistical modeling can deal with scenes with complex and time varying illumination. S models are constructed and updated for every inputted frame. Results obtained with different indoor and outdoor sequences show the robustness of the approach.


  • W.Hu et al, “A Survey on Visual Surveillance of Object Motion and Behaviors”, IEEE Transactions on SMC - part C: Applications and reviews, Vol. 34, No.3.
  • Thomas B. Moeslund et al,”A Survey of Advances in Vision Based Human Motion Capture and Analysis”, Computer Vision and Image Understanding, October 2006.
  • Joshua Migdal and W.E.L. Grimson,”Background Subtraction Using Markov Thresholds”, Proceedings of the IEEE Workshop on Motion and Video Computing, 2005.
  • J. Stauder, R. Mech and J. Ostermann. “Detection of Moving Cast Shadows for Object Segmentation”. IEEE Transactions on Multimedia, 1(1):65-76,1999.
  • Kameda, Yoshinari et al, ”A human motion estimation method using three successive video frames”, proceedings of the IC on virtual system and multimedia. 1996.
  • S.Wachter and H.H. Nagel, “Tracking Persons in Monocular Image Sequences”, Computer Vision and Image Understanding, Vol. 74, No.3, pp. 174-192, June, 1999.
  • Chia-Jung Pai et al,“Pedestrian Detection and Tracking at Crossroads”, Pattern Recognition, 2004.
  • N.Thome et al, “A Robust Appearance Model for Tracking Human Motions”, IEEE DICTA-2005.
  • L.Havasi et al, “Higher Order Symmetry for Non-linear Classification of Human Walk Detection”, Pattern Recognition Letters, Vol. 27, pp. 822-829, 2006.
  • S.Denman et al, (2005), “Adaptive Optical Flow for Person Tracking”, IEEE DICTA-2005.
  • Daniel Freedman and M.W.Turek,”Illumination-Invariant Tracking via Graph Cuts”, IEEE CVPR, June 2005.
  • Steven Cheng, et al,”A MultiScale Parametric Background Model for Stationary Foreground Object Detection”, IEEE Workshop on Motion and Video Computing, 2007.
  • Mohand Said Allili, et al,”A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling”, IEEE Fourth Candian Conference on Computer and Robot Vision, 2007.
  • Jaime Gallego, et al,”Segmentation and tracking of static and moving objects in video surveillance scenarious”, IEEE ICIP, 2008.
  • Stephen Bernstein and Ruth Bernstein, “Elements of Statistics II: Inferential Statistics”. Schaum’s Outlines, First edition, New Delhi, 2005.
  • Murray R. Spiegel and Larry J. Stephens, “Statistics”. Schaum’s Outlines, Third edition, New Delhi, 2000.
  • Collins et al, ”A system for video surveillance and monitoring”, tech. report CMU-RI-TR-00-12, Robotics Institute, Carnegie Mellon University, May, 2000.
  • Andrew Woo, Pierre Poulin and Alain Fournier, “A survey of shadow algorithms”, IEEE CG&A, Volume 10, Issue 6, Nov. 1990 Page(s):13 - 32 November 1990.
  • Caixia Jiang and M.O. Ward, “Shadow identification”, Proceedings of the IEEE Computer Society Conference on CVPR, 15-18 June 1992 Page(s):606 – 612.
  • Elena Salvador, Andrea Cavallaro and Touradj Ebrahimi, “Shadow identification and classification using color models”, Proceedings of the IEEE IC on Acoustics, Speech, and Signal Processing, Volume 3, 7-11 May 2001 Page(s):1545 - 1548
  • Yinlong Sun, “Self shadow and local illumination of randomly rough surfaces”, Proceedings of the 2004 IEEE Computer Society conference on Computer Vision and Pattern Recognition, 2004.
  • Wang. J.M, “Shadow detection and removal for traffic images”, Proceedings of the IEEE international Conference on Networking, Sensing & Control, 2004.
  • Takeshi Takai, A Maki and T Matsuyama, “Self shadows and cast shadows in estimating illumination distribution”, , 4th European Conference on Visual Media Production 27-28 Nov. 2007 Page(s):1 - 10
  • Li Xu, Feihu Qi and Renjie Jiang, “Shadow removal from a single image”, Proceedings of the IEEE international Conference on Intelligent Systems Design and applications, 2006.
  • Douglas C. Montgomery and Geroge C. Runger, “Applied Statistics and probability for engineers”, Third edition, John wiley & Sons, 2003.Stephen B and Ruth B, “Elements of Statistics II: Inferential Statistics”, Schaum’s Outlines, Tata McGraw-Hill Edition, 2005.
  • Prem S.Mann, “Introductory Statistics”, Fifth edition, Wiley India Edition, 2007.
  • Prati, I. Mikic, M.M. Trivedi and R. Cucchira. “Detecting Moving Shadows: Algorithms and evaluation”. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol.25. No.7, July 2003.
  • Prati, I. Mikic, M.M. Trivedi and R. Cucchira. “Detecting Moving Objects, Ghots and Shadows in video streams”. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol.25. No.10, October 2003.
  • Jun-Wei Hsieh, Shih-Hao Yu, Yung-Sheng Chen, and Wen-Fong Hu. “A Shadow Elimination Method for Vehicle Analysis”. Proceedings of the 17th IEEE International Conference on Pattern Recognition, 2004.
  • Kuo-Hua Lo and Mau-Tsuen Yang, “Shadow Detection by Integrating Multiple Features”, Proceedings of the IEEE 18th International Conference on Pattern Recognition, 2006.