An Intelligent Moving Object Segmentation Using Hybrid IFCM-CSS Clustering Model

Abstract

In this study, a novel hybrid deep clustering approach is proposed for the effective moving object segmentation. Initially, the data is collected, and the keyframe selection is performed using the threshold-based Kennard–Stone method. Then, the preprocessing step involves noise filtering using bilateral wavelet thresholding and binary color conversion. The blob detection is performed using normalized Laplacian of Gaussian. Finally, the segmentation of moving objects is performed using a hybrid clustering approach called improved fuzzy C-mean (IFCM) clustering with chaotic salp swarm (CSS) optimization algorithm (Hybrid IFCM-CSS). The overall evaluation is done in MATLAB. The performance of the hybrid IFCM-CSS is compared to other approaches based on some measures. The proposed Hybrid IFCM-CSS achieves the highest precision of 0.971, using the SBM-RGBD dataset.