Motion data segmentation using robust subspace clustering with noise suppression

Numerous applications regard motion segmentation as a fundamental and vital process. A plethora of motion segmentation techniques have been introduced, with the subspace clustering-based method standing out, particularly because of its unsupervised nature. However, these methods often face a challenge in effectively handling nonlinear data with hybrid noise. In the present study, we propose a novel robust subspace clustering methodology, specifically designed to address the complexities inherent in motion segmentation tasks. We've termed it as Robust Subspace Clustering with Noise Suppression (RSCNS),which integrates hybrid noise reconstruction with a representation of data relationships. Specifically, we propose a hybrid noise modeling method by joining Correntropy and Cauchy function to suppress noise and outlier pollution. To restore the corrupted data, we treat the motion trajectory feature data matrix as an approximate low-rank matrix and design a truncated weighting nuclear norm regularization constraint. Meanwhile, the block diagonal regularizer (BDR) is incorporated into our model to ensure that motion trajectory features from the same moving object are clustered together. Experimental evaluations are conducted on various video datasets, demonstrating that RSCNS can effectively handle motion segmentation tasks not only in visible light video, but also in invisible light (infrared) video.