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

Abstract

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%.