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Background Subtraction in Surveillance systems- A Neural Fuzzy Approach

Joydip Dhar, Ritika Kurele, Surbhi Arora, Swati Sinha


Surveillance systems mostly deal with moving object detection. In this paper we propose a neural-fuzzy based method to detect object in the dynamic background. The neural approach is based on self-organizing map (SOM) architecture (unsupervised learning) in which every pixel is mapped to the 3x3 neural map. The threshold parameters required to consider a pixel as an object or part of background are calculated by fuzzy inference system independent of human intervention. The proposed approach gives robust results of the videos taken by stationery cameras considering scenes containing moving ackground, illumination changes, camouflage and has no bootstrapping limitations. The results obtained by using SOM with fuzzy inference system (mamdani) are compared with SOM with manual parameters in terms of detection accuracy.


Background subtraction, Neural-Fuzzy segmentation, Self-organising map, Video surveillance, Mamdani-inference system

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