Abstract
In the field of image processing, Gaussian mixture model (GMM) is always used to detect and recognize moving objects. Due to the defects of GMM, there are some error detections in the final consequence. In order to eliminate the defects of GMM in moving objects detections, this paper has studied a moving object detection algorithm, combining GMM with scale-invariant feature transform (SIFT) keypoint match. First, GMM is built to obtain the distributions of background image pixels. Then, morphological processing method is applied to improve the quality of binary segmentation image and extract segmentation images of moving objects. Finally, SIFT keypoint match algorithm is used to eliminate misjudgment segmentation images by judging whether the segmentation image matches with the background template or not. Compared with original GMM, the results show that the accuracy of moving object detection has been improved.
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References
Pietikäinen, M., Hadid, A., Zhao, G., et al.: Background subtraction. Surf. Sci. 461(1–3), 1–15 (2011)
Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104. IEEE (2005)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Detection, vol. 2, pp. 246–252. IEEE Xplore (1999)
KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Remagnino, P., Jones, G.A., Paragios, N., Regazzoni, C.S. (eds.) Video-Based Surveillance Systems, pp. 135–144. Springer, Boston (2002). https://doi.org/10.1007/978-1-4615-0913-4_11
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Kluwer Academic Publishers, Dordrecht (2004)
Lowe, D.G.: Object detection from local scale-invariant features. In: The Proceedings of the 7th IEEE International Conference on Computer Vision. IEEE (2002)
Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Detection, ICPR 2004, vol. 2. IEEE (2004)
Wang, X., Ma, X., Grimson, W.E.: Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 539–555 (2009)
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Dong, H., Zhang, X. (2018). Moving Object Detection Algorithm Using Gaussian Mixture Model and SIFT Keypoint Match. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_3
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DOI: https://doi.org/10.1007/978-3-319-73564-1_3
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