Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach

Sensors

Mohd Asyraf Zulkifley¹  Bill Moran² 

[1] Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia;Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia; E-Mails:

关键词: foreground detection;    shadow removal;    Gaussian modelling;    colour co-occurrence;    conditional random field;    edge-based modelling;    colour constancy;   

DOI  :  10.3390/s120505623

来源: mdpi

【 摘 要 】

Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good in filtering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.