An error compensation based background modeling method for complex scenarios

被引：0
|
作者：
Qin M. [1 ,2 ]
Lu Y. [1 ,2 ]
Di H.-J. [1 ,2 ]
Lv F. [1 ,2 ]
机构：
[1] School of Computer Science, Beijing Institute of Technology, Beijing
[2] Beijing Laboratory of Intelligent Information Technology, Beijing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2016年 / 42卷 / 09期
基金：
高等学校博士学科点专项科研基金; 中国国家自然科学基金;
关键词：
Alpha-mating; Anti-interference error compensation; Background modeling; Median template; Spatial continuity;
D O I：
10.16383/j.aas.2016.c150857
中图分类号：
学科分类号：
摘要：
Compensating foreground error with background information usually helps to build an accurate background model for the subspace learning based background modeling method. However, dynamic background (swaying tree or waving water surface) and complex foreground signal may have bad influences on the compensation process. To solve the problem, we propose an error compensation based incremental subspace method for background modeling, which aims to build an accurate background model in complex scenarios. First, we bring a spatial continuity constraint to the foreground error estimation process, which helps to preserve more dynamic background information and increase the accuracy of the background model. Second, we formulate the foreground estimation task into a convex optimization problem, and design an accurate optimization algorithm and a fast optimization algorithm, respectively for different applications. Third, an alpha-mating based error compensation strategy is designed, which increases the anti-interference performance of our algorithm. At last, a median background template which does not rely on background model is constructed, which increases the robustness of our algorithm. Multiple experiments show that the proposed method is able to model background accurately even in complex scenarios, demonstrating the anti-interference performance and the robustness of our method. Copyright © 2016 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：1356 / 1366
页数：10
相关论文
共 26 条
  • [1] Huo D.-H., Yang D., Zhang X.-H., Hong M.-J., Principal component analysis based codebook background modeling algorithm, Acta Automatica Sinica, 38, 4, pp. 591-600, (2012)
  • [2] Chu J., Yang F., Zhang G.-M., Wang L.-F., A stepwise background subtraction by fusion spatio-temporal information, Acta Automatica Sinica, 40, 4, pp. 731-743, (2014)
  • [3] Wang Y.-Z., Liang Y., Pan Q., Cheng Y.-M., Zhao C.-H., Spatiotemporal background modeling based on adaptive mixture of Gaussians, Acta Automatica Sinica, 35, 4, pp. 371-378, (2009)
  • [4] Stauffer C., Grimson W.E.L., Adaptive background mixture models for real-time tracking, Proceedings of the 14th IEEE Conference on Computer Vision and Pattern Recognition, pp. 246-252, (1999)
  • [5] Elgammal A.M., Harwood D., Davis L.S., Non-parametric model for background subtraction, Proceedings of the 6th European Conference on Computer Vision, pp. 751-767, (2000)
  • [6] Candes E., Li X.D., Ma Y., Wright J., Robust principal component analysis?, Journal of the ACM, 58, 3, (2011)
  • [7] Zhou T.Y., Tao D.C., GoDec: randomized low-rank & sparse matrix decomposition in noisy case, Proceedings of the 28th International Conference on Machine Learning, pp. 33-40, (2011)
  • [8] Dikmen M., Huang T.S., Robust estimation of foreground in surveillance videos by sparse error estimation, Proceedings of the 19th International Conference on Pattern Recognition, pp. 1-4, (2008)
  • [9] Xue G.J., Song L., Sun J., Wu M., Foreground estimation based on robust linear regression model, Proceedings of the 18th International Conference on Image Processing, pp. 3269-3272, (2011)
  • [10] Xue G.J., Song L., Sun J., Foreground estimation based on linear regression model with fused sparsity on outliers, IEEE Transactions on Circuits and Systems for Video Technology, 23, 8, pp. 1346-1357, (2013)
123