Robust background modelling using region-based codebooks

被引:0
作者
机构
[1] Indian Institute of Technology Guwahati, Guwahati, Assam
[2] Department of Computer Science, Loughborough University
来源
Schaefer, G. (gerald.schaefer@ieee.org) | 1600年 / Inderscience Enterprises Ltd.卷 / 05期
关键词
Background modelling; Background subtraction; Segmentation; Video analysis;
D O I
10.1504/IJAMC.2014.060507
中图分类号
学科分类号
摘要
Dynamic backgrounds and sudden illumination changes are two of the major problems associated with background subtraction techniques. In this paper, wepresent a novel approach to background subtraction that addresses both of these challenges. Based on the work of Kim et al., we develop an improved codebook background modelling and subtraction technique.We utilise image segmentation on the background image and model the background with a codebook for each pixel along with a pseudo background layer.We perceive background motion as an occlusion of one background layer by a nearby background layer. In other words, sliding of one background layer over a neighbouring layer causes background motion and will hence result in false segmentation. We present our approach of codeword spreading across layer boundaries to handle background motion and further propose a two-step update of the background codebook to handle both sudden and gradual illumination changes. Experimental results confirm the efficacy of our technique. Copyright © 2014 Inderscience Enterprises Ltd.
引用
收藏
页码:233 / 244
页数:11
相关论文
共 17 条
  • [1] Barnich O., Van Droogenbroeck M., Vibe: A universal background subtraction algorithm for video sequences, IEEE Trans. Image Processing, 20, 6, pp. 1709-1724, (2011)
  • [2] Bicego M., Cristani M., Murino V., Unsupervised scene analysis: A hidden markov model approach, Computer Vision and Image Understanding, 102, 1, pp. 22-41, (2006)
  • [3] Comaniciu D., Meer P., Mean shift: A robust approach toward feature space analysis, IEEE Trans. Pattern Analysis and Machine Intelligence, 24, pp. 603-619, (2002)
  • [4] Dalley G., Migdal J., Grimson W.E.L., Background subtraction for temporally irregular dynamic textures, IEEE Workshop on Application of Computer Vision, pp. 1-7, (2008)
  • [5] Elgammal A.M., Duraiswami R., Harwood D., Davis L.S., Background and foreground modeling using nonparametric kernel density estimation for visual surveillance, Proceedings of the IEEE, 90, pp. 1151-1163, (2002)
  • [6] Haritaoglu I., Harwood D., Davis L.S., W4: Real-time surveillance of people and their activities, IEEE Trans. Pattern Analysis and Machine Intelligence, 22, 8, pp. 809-830, (2000)
  • [7] Jojic N., Frey B.J., Learning flexible sprites in video layers, IEEE Int. Conference Computer Vision and Pattern Recognition, 1, pp. 199-206, (2001)
  • [8] Kim K., Chalidabhongse T.H., Harwood D., Davis L.S., Real-time foreground- background segmentation using codebook model, Real-Time Imaging, 11, 3, pp. 172-185, (2005)
  • [9] Kim K., Harwood D., Davis L.S., Background updating for visual surveillance, Int. Symposium on Visual Computing, pp. 337-346, (2005)
  • [10] Li L., Huang W., Gu I.Y.H., Tian Q., Statistical modeling of complex backgrounds for foreground object detection, IEEE Trans. Image Processing, 13, 11, pp. 1459-1472, (2004)