CFP last date
20 December 2024
Reseach Article

Cast and Self Shadow Segmentation in Video Sequences using Interval based Eigen Value Representation

by Chandrajit M., Girisha R., Vasudev T., Ashok C.B.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 4
Year of Publication: 2016
Authors: Chandrajit M., Girisha R., Vasudev T., Ashok C.B.
10.5120/ijca2016909752

Chandrajit M., Girisha R., Vasudev T., Ashok C.B. . Cast and Self Shadow Segmentation in Video Sequences using Interval based Eigen Value Representation. International Journal of Computer Applications. 142, 4 ( May 2016), 27-32. DOI=10.5120/ijca2016909752

@article{ 10.5120/ijca2016909752,
author = { Chandrajit M., Girisha R., Vasudev T., Ashok C.B. },
title = { Cast and Self Shadow Segmentation in Video Sequences using Interval based Eigen Value Representation },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 4 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume142/number4/24886-2016909752/ },
doi = { 10.5120/ijca2016909752 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:44:04.692385+05:30
%A Chandrajit M.
%A Girisha R.
%A Vasudev T.
%A Ashok C.B.
%T Cast and Self Shadow Segmentation in Video Sequences using Interval based Eigen Value Representation
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 4
%P 27-32
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Tracking of motion objects in the surveillance videos is useful for the monitoring and analysis. The performance of the surveillance system will deteriorate when shadows are detected as moving objects. Therefore, shadow detection and elimination usually benefits the next stages. To overcome this issue, a method for detection and elimination of shadows is proposed. This paper presents a method for segmenting moving objects in video sequences based on determining the Euclidian distance between two pixels considering neighborhood values in temporal domain. Further, a method that segments cast and self shadows in video sequences by computing the Eigen values for the neighborhood of each pixel is proposed. The dual-map for cast and self shadow pixels is represented based on the interval of Eigen values. The proposed methods are tested on the benchmark IEEE CHANGE DETECTION 2014 dataset.

References
  1. Zhang, D. and Lu, G. (2001) Segmentation of Moving Objects in Image Sequence: A Review, Circuits, Systems and Signal Process, vol. 20(2), pp. 143-183.
  2. Prati, A., Cucchiara, R., Mikic I. and Trivedi, M. (2001) Analysis and Detection of Shadows in Video Streams: A Comparative Evaluation, IEEE Trans.
  3. Sanin, A., Sanderson, C. and Lovell, B.C. (2012) Shadow detection: A survey and comparative evaluation of recent methods, ELSEVIER, Pattern Recognition, vol. 45, pp. 1684–1695.
  4. Ullah, H., Ullah, M., Uzair M. and Rehman, F. (2010) Comparative Study: The Evaluation of Shadow Detection Methods, International Journal of Video & Image Processing and Network Security, vol. 01, no. 10.
  5. Heikkila J. and Silven, O. (1991) A real-time system for monitoring of cyclists and pedestrians, Second IEEE Workshop on Visual Surveillance Fort Collins, Colorado, pp. 74-81.
  6. Armanfard, N., Komeili, M. and Kabir, E. (2012) TED: A texture-edge descriptor for pedestrian detection in video sequences, ELSEVIER, Pattern Recognition, vol. 45, pp. 983–992.
  7. Elgammal, A., Duraiswami, R. Harwood, D. and Davis, L.S. (2002) Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance, Proceedings of the IEEE, vol. 90(7).
  8. Johnsen S. and Tews, A. (2009) Real-Time Object Tracking and Classification Using a Static Camera, Proceedings of the IEEE ICRA.
  9. Rafael Munoz-Salinas, (2008) A Bayesian plan-view map based approach for multiple-person detection and tracking, ELSEVIER, Pattern Recognition, vol. 41, pp. 3665–3676.
  10. Stauffer, C. and Grimson, W.E.L. (1999) Adaptive background mixture models for real-time tracking, Proc. IEEE CVPR 1999, pp. 24&252.
  11. Chao-Yang Lee, Shou-Jen Lin, Chen-Wei Lee and Chu-Sing Yang. (2012) An efficient continuous tracking system in real-time surveillance application, ELSEVIER, Journal of Network and Computer Applications, vol. 35, pp. 1067–1073.
  12. Fang-Hsuan Cheng and Yu-Liang Chen, (2006) Real time multiple objects tracking and identification based on discrete wavelet transform, ELSEVIER, Pattern Recognition, vol. 39, pp. 1126-1139.
  13. Jung-Ho Ahn, Choi, C., Kwak, S. Kim, K. and Byun, H. (2009) Human tracking and silhouette extraction for human-robot interaction systems, Springer-Verlag, Pattern Anal. Appl., vol 12(2), pp 167-177.
  14. Denman, S., Fookes, C. and Sridharan, S. (2010) Group Segmentation During Object Tracking using Optical Flow Discontinuities, Proceedings of IEEE Image and Video Technology (PSIVT), pp.270,275, 14-17.
  15. Chandrajit, M., Girisha, R., and Vasudev, T. (2014a) Motion segmentation from surveillance videos using T-test statistics. In Proceedings of the 7th ACM India Computing Conference (COMPUTE '14). ACM, New York, NY, USA, Article 2, 10 pages. DOI=10.1145/2675744.2675748 http://doi.acm.org/10.1145/2675744.2675748.
  16. Chandrajit, M., Girisha, R., Vasudev, T. (2014b) “Motion Segmentation from Surveillance Video Sequences using Chi-Square Statistics”, Proceedings of the second International Conference on - Emerging Research in Computing, Information, Communication and Applications , ERCICA 2014, (Vol 2) 365-372, Elsevier, ISBN 9789351072621.
  17. Girisha R. and Murali, S. (2009) Segmentation of Motion Objects from Surveillance Video Sequences Using Temporal Differencing Combined with Multiple Correlation, Advanced Video and Signal Based Surveillance. Proc. IEEE AVSS '09, pp.472,477, 2-4.
  18. Lim, T., Han B., and Han, J. H. (2012) Modeling and segmentation of floating foreground and background in videos, ELSEVIER, Pattern Recognition, vol. 45, pp. 1696–1706.
  19. Liu, C., Yuen P.C., and Qiu, G. (2009) Object motion detection using information theoretic spatio-temporal saliency, ELSEVIER, Pattern Recognition, vol. 42, pp. 2897-2906.
  20. Lopez, M.T., Fernandez-Caballero, A., Fernandez, M.A., Mira J. and Delgado, A.E. (2006) Visual surveillance by dynamic visual attention method, ELSEVIER, Pattern Recognition, vol. 39, pp. 2194–2211.
  21. Yu-Ting Chen, Chu-Song Chen, Chun-Rong Huang and Yi-Ping Hung, (2007) Efficient hierarchical method for background subtraction, ELSEVIER, Pattern Recognition, vol. 40, pp. 2706–2715.
  22. Amato, A., Mozerov, M.G., Bagdanov, A.D. and Gonzalez, J. (2011) "Accurate Moving Cast Shadow Suppression Based on Local Color Constancy Detection," Image Processing, IEEE Transactions on , vol.20, no.10, pp.2954,2966.
  23. Girisha, R. and Murali, S. (2009) Adaptive Cast Shadow Elimination Algorithm for Surveillance Videos Using t Random Values, Proceedings of India Conference.
  24. Girisha, R. and Murali, S. (2010) Self shadow elimination algorithm for surveillance videos using ANOVA F test, Proceedings of the Third Annual ACM Bangalore Conference.
  25. Jia-Bin Huang, Chu-Song Chen, (2009) "Moving cast shadow detection using physics-based features," Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on , vol., no., pp.2310,2317, 20-25.
  26. Leone, A. and Distante, C. (2007) Shadow detection for moving objects based on texture analysis, ELSEVIER, Pattern Recognition, vol. 40, pp. 1222–1233.
  27. Souza, T., Schnitman, L. and Oliveira, L. (2012) EIGEN ANALYSIS AND GRAY ALIGNMENT FOR SHADOW DETECTION APPLIED TO URBAN SCENE IMAGES, In: IEEE International Conference on Intelligent Robots and Systems (IROS) Workshop on Planning, Perception and Navigation for Intelligent Vehicles.
  28. Chia-Chih Chen; Aggarwal, J.K. (2010) "Human Shadow Removal with Unknown Light Source," Pattern Recognition (ICPR), 2010 20th International Conference on , vol., no., pp.2407,2410, 23-26.
  29. Nadimi, S., Bhanu, B. (2004) "Physical models for moving shadow and object detection in video," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.26, no.8, pp.1079,1087.
  30. Wang, Y., Tan, T., Kia-FockLoe and Jian-Kang Wu. (2005) A probabilistic approach for foreground and shadow segmentation in monocular image sequences, ELSEVIER, Pattern Recognition, vol. 38, pp. 1937–1946.
  31. Soille, P. (1999) Morphological Image Analysis: Principles and Applications, Springer-Verlag, pp. 173-174.
  32. Goyette, Jodoin, P.M., Porikli, F., Konrad J. and Ishwar, P. (2012). changedetection.net: A new change detection benchmark dataset, in Proc. IEEE Workshop on Change Detection (CDW-2012) at CVPR-2012, Providence, RI, 16–21.
  33. Powers, D. M. W. (2007) 'Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation' (SIE-07-001), Technical report, School of Informatics and Engineering, Flinders University, Adelaide, Australia.
  34. Van Rijsbergen, C. J. (1979), Information Retrieval (2nd Ed.). Butterworth.
Index Terms

Computer Science
Information Sciences

Keywords

Motion segmentation Eigen values Shadow detection Shadow segmentation Self shadow Cast shadow.