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High-quality real-time moving object detection by non-parametric segmentation

High-quality real-time moving object detection by non-parametric segmentation

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Usually, moving object detection by exclusively background modelling is not enough to separate correctly background and foreground. On the other hand, strategies based on both background and foreground models are complex and computationally inefficient, and therefore need to be optimised for real-time performance. A novel and fast strategy for moving object detection by non-parametric background-foreground modelling is proposed. Whereas the background is modelled using only colour information, both colour and space are considered for the foreground modelling. Moreover, application of an efficient iterative multi-tracking algorithm allows the update of the spatial information, thus enabling an important reduction in computational requirements and improving the segmentation results.

References

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      • S.Y. Elhabian , K.M. El-Sayed , S.H. Ahmed . Moving object detection in spatial domain using background removal techniques-state-of-art. Recent Patents on Computer Science , 1 , 32 - 54
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      • Y. Sheikh , M. Shah . Bayesian modeling of dynamic scenes for object detection. IEEE Trans. Pattern Anal. Appl. , 11 , 1778 - 1792
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      • Martel-Brison, N., Zaccarin, A.: `Unsupervised approach for building non-parametric background and foreground models of scenes with significant foreground activity', ACM Workshop on Vision Networks for Behaviour Analysis, 2008.
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