PedCut: an iterative framework for pedestrian segmentation combining shape models and multiple data cues

This paper presents an iterative, EM-like framework for accurate pedestrian segmentation, combining generative shape models and multiple data cues. In the E-step, shape priors are introduced in the unary terms of a Conditional Random Field (CRF) formulation, joining other data terms derived from color, texture and disparity cues. In the M-step, the resulting segmentation is used to adapt an Active Shape Model (ASM), after which the EM process alternates.
Experiments on the public Penn-Fudan pedestrian dataset suggest that our method outperforms the state-of-the-art. We further provide results on a new Daimler pedestrian dataset, captured from on-board a vehicle, which includes disparity data. This dataset is made public to facilitate benchmarking.