REAL-TIME MOVING OBJECT DETECTION IN VIDEO SEQUENCES USING SPATIO-TEMPORAL ADAPTIVE GAUSSIAN MIXTURE MODELS

Katharina Quast, Matthias Obermann, André Kaup

2010

Abstract

In this paper we present a background subtraction method for moving object detection based on Gaussian mixture models which performs in real-time. Our method improves the traditional Gaussian mixture model (GMM) technique in several ways. It takes into account spatial and temporal dependencies, as well as a limitation of the standard deviation leading to a faster update of the model and a smoother object mask. A shadow detection method which is able to remove the umbra as well as the penumbra in one single processing step is further used to get a mask that fits the object outline even better. Using the computational power of parallel computing we further speed up the object detection process.

References

  1. Aach, T. and Kaup, A. (1995). Bayesian algorithms for change detection in image sequences using Markov random fields. Signal Processing: Image Communication, 7(2):147-160.
  2. Carminati, L. and Benois-Pineau, J. (2005). Gaussian mixture classification for moving object detection in video surveillance environment. In Proc. IEEE International Conference on Image Processing, volume 3.
  3. KaewTraKulPong, P. and Bowden, R. (2001). An improved adaptive background mixture model for realtime tracking with shadow detection. In Proc. 2nd European Workshop Advanced Video Based Surveillance Systems, volume 1.
  4. Li, L., Huang, W., Gu, I., and Tian, Q. (2004). Statistical modeling of complex backgrounds for foreground object detection. IEEE Transactions on Image Processing, 13(11):1459-1472.
  5. Porikli, F. and Tuzel, O. (2003). Human body tracking by adaptive background models and mean-shift analysis. In Proc. IEEE International Workshop on Performance Evaluation of Tracking and Surveillance.
  6. Power, P. W. and Schoonees, J. A. (2002). Understanding background mixture models for foreground segmentation. In Proc. Image and Vision Computing, pages 267-271.
  7. Stauffer, C. and Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2.
  8. Yang, S. and Hsu, C. (2006). Background modeling from gmm likelihood combined with spatial and color coherency. In Proc. IEEE International Conference on Image Processing.
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Paper Citation


in Harvard Style

Quast K., Obermann M. and Kaup A. (2010). REAL-TIME MOVING OBJECT DETECTION IN VIDEO SEQUENCES USING SPATIO-TEMPORAL ADAPTIVE GAUSSIAN MIXTURE MODELS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 413-418. DOI: 10.5220/0002816904130418


in Bibtex Style

@conference{visapp10,
author={Katharina Quast and Matthias Obermann and André Kaup},
title={REAL-TIME MOVING OBJECT DETECTION IN VIDEO SEQUENCES USING SPATIO-TEMPORAL ADAPTIVE GAUSSIAN MIXTURE MODELS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={413-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002816904130418},
isbn={978-989-674-028-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - REAL-TIME MOVING OBJECT DETECTION IN VIDEO SEQUENCES USING SPATIO-TEMPORAL ADAPTIVE GAUSSIAN MIXTURE MODELS
SN - 978-989-674-028-3
AU - Quast K.
AU - Obermann M.
AU - Kaup A.
PY - 2010
SP - 413
EP - 418
DO - 10.5220/0002816904130418