Unifying Boundary, Region, Shape into Level Sets for Touching Object Segmentation in Train Rolling Stock High Speed Video

Traditional level sets suffer from two major limitations: 1) unable to detect touching object boundaries and 2) segment partially occluded objects. In this paper, we present a model and simulation of a level set functional with unified knowledge of objects region, boundary, and shape models. The simulations of the proposed model were tested on high-speed videos of the train rolling stock for bogie part segmentation. The proposed model will resolve single- and multi-object segmentation of touching boundaries and partially occulted mechanical parts on a train bogie. Simulations on high-speed videos of four trains with 1 0720 frames have resulted in near perfect segmentation of 10 touching and occluded bogie parts. The proposed model performed better than the state-of-the-art level set segmentation models, showing faster and more accurate segmentations of moving mechanical parts in high-speed videos.