Browse > Article

Fusion of Background Subtraction and Clustering Techniques for Shadow Suppression in Video Sequences  

Chowdhury, Anuva (Chittagong University)
Shin, Jung-Pil (University of Aizu)
Chong, Ui-Pil (University of Ulsan)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.14, no.4, 2013 , pp. 231-234 More about this Journal
Abstract
This paper introduces a mixture of background subtraction technique and K-Means clustering algorithm for removing shadows from video sequences. Lighting conditions cause an issue with segmentation. The proposed method can successfully eradicate artifacts associated with lighting changes such as highlight and reflection, and cast shadows of moving object from segmentation. In this paper, K-Means clustering algorithm is applied to the foreground, which is initially fragmented by background subtraction technique. The estimated shadow region is then superimposed on the background to eliminate the effects that cause redundancy in object detection. Simulation results depict that the proposed approach is capable of removing shadows and reflections from moving objects with an accuracy of more than 95% in every cases considered.
Keywords
Background subtraction; K-means clustering; shadow removal; video sequence;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ahmed M.N., Yamany S.M., Mohamed N., Farag A.A., Moriarty T," A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data,", IEEE Trans. on Medical Imaging, vol. 21, 2002, pp. 193-199.   DOI
2 Zhang D.Q., Chen S.C., Pan Z. S., Tan K.R., "Kernel-Based Fuzzy Clustering Incorporating Spatial Constraints for Image Segmentation," in Proc. International Conference on Machine Learning and Cybernetics, 2003, pp. 2189-2192.
3 Stauffer C., Grimson W., "Mean-shift background image modeling," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, 2000, pp.747-757.   DOI
4 Anurag M., Nikos P., "Motion based background subtraction using adaptive kernel density estimation," in Proc. Computer vision and patter recognition, 2004, pp. 302-309.
5 Piccardi M., Jan T., "Mean-shift background image modeling," in Proc. IEEE International Conf. on Image Processing, Singapore, 2004, pp. 3399-3402.
6 Zoran Z., "Improved Adaptive Gaussian Mixture Model for Background Subtraction," in Proc. ICPR, 2004.
7 Jwu-Sheng H., Tzung-Min S., "Robust Background Subtraction with Shadow and Highlight Removal for Indoor Surveillance," Journal on Adv Signal Processing, 2007, pp.1-14.
8 Parisa Darvish Zadeh V., Michael S.-L., Guillaume-Alexandre B., "An Efficient Region-Based Background Subtraction Technique," in Proc.Canadian Conference on Computer and Robot Vision, CRV'08, May 2008, pp. 71 -78.
9 Tang Z., Miao Z. Wan Y., "Background Subtraction Using Running Gaussian Average and Frame Difference," Journal of International Federation for Information Processing (IFIP) , vol.4740, 2007, pp.411-414.
10 Te-Feng S., Yi-Ling C., Shang-Hong L., "Over-Segmentation Based Background Modeling and Foreground Detection with Shadow Removal by Using Hierarchical MRFs," in Proc. Computer Vision-ACCV, 2010, pp. 535-546.