Pearson's correlation and background subtraction (BGS) based approach for object's motion detection in infrared video frame sequences
Author:
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s00362-022-01323-x.pdf
Reference65 articles.
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2. Babaee M, Dinh DT, Rigoll G (2018) A deep convolutional neural network for video sequence background subtraction. Pattern Recogn 76:635–649. https://doi.org/10.1016/j.patcog.2017.09.040
3. Barnich O, Van Droogenbroeck M (2011) ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724. https://doi.org/10.1109/tip.2010.2101613
4. Bouwmans T (2012) Background subtraction for visual surveillance: a fuzzy approach. In: Handbook on soft computing for video surveillance, pp 103–138. https://doi.org/10.1201/b11631-6.
5. Bouwmans T, El Baf F, Vachon B (2010) Statistical background modeling for foreground detection: a survey. In: Handbook of pattern recognition and computer vision, pp 181–199. https://doi.org/10.1142/9789814273398_0008
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