Abstract
This paper applies online RPCA framework on color and depth data for background modeling and foreground segmentation. First, it could model a better background scene for video sequence in real-time. Second, it is a refinement for foreground segmentation. In this paper, depth data and color data are processed separately under online RPCA method, the structured sparse matrix are combined together using data fusion method for better foreground segmentation. Experiments show that our combined foreground results have a better and robust performance than results with one way alone.
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References
Fernandez-Sanchez EJ, Rubio L (2014) Background subtraction model based on color and depth cues. Mach Vis Appl 25(5):1211–1225
Camplani M, del Blanco CR (2002) Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction. Household Appliances Mag (3):20–21
Tian D, Mansour H, Vetro A (2006) Depth-weighted group-wise principal component analysis for video foreground/background separation. Mech Eng (5):64–66
Nguyen, V-T, Vu, H (2006) An Efficient Combination of RGB and Depth for Background Subtraction. Mech Eng (5):64–66
Liang Z, Liu X, Liu H (2006) A refinement framework for background subtraction based on colour and depth data. Mech Eng (5):64–66
Fu H, Wang B, Liu H (2018) Online RPCA on Background Modeling. In: Jia Y, Du J, Zhang W (eds) proceedings of 2018 Chinese intelligent systems conference. Lecture notes in electrical engineering, vol 529. Springer, Singapore
Fu H, Wang B, Liu H (2017) Fast robust PCA on background modeling. In: Jia Y, Du J, Zhang W (eds) proceedings of 2017 Chinese intelligent systems conference. Lecture notes in electrical engineering, vol 460. Springer, Singapore
Zhou T, Tao D (2011) GoDec: randomized lowrank & sparse matrix decomposition in noisy case. In proceedings of the 28th international conference on machine learning, pp 33–40
Camplani M, Salgado L (2014) Background foreground segmentation with RGBD kinect data: an efficient combination of classifiers. J Vis Commun Image Represent 25:122–136
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Fu, H., Liu, H. (2020). Online RPCA Background Modeling Based on Color and Depth Data. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_57
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DOI: https://doi.org/10.1007/978-981-32-9698-5_57
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9697-8
Online ISBN: 978-981-32-9698-5
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