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A Novel Approach for Detection of Moving Objects in Complex Scenes Using Fuzzy Colour Difference Histogram

Background subtraction schematic is widely used for motion detection. For effective automation of this process, a robust algorithm with high accuracy is needed. One of the major challenges of such algorithms is the identification of objects from an environment with composite elements that may be a dynamic background, frames with a camouflaged background and foreground pixels, and consecutive frames with varying illumination. The existing system uses a multi-color space histogram superposition principle having the biggest challenge of choosing appropriate color components in suitable proportion. Overcoming this challenge, a novel approach, MODITBS, processed in a differential domain, is proposed. A fuzzified color difference histogram-based background modeling is done to significantly deal with complex background scenes followed by principal component analysis-based feature extraction. The foreground objects detected are enhanced using a Kalman filter. The results show that MODITBS attains an accuracy of 95.16% in comparison to the existing system having an accuracy of 91.25%.