- PedCut: an iterative framework for pedestrian segmentation combining shape models and multiple data cues
- British Machine Vision Conference 2013
- Book/source title
- Proceedings of the British Machine Vision Conference: BMVC 2013: Bristol, 9-13 Sept
- Pages (from-to)
- BMVA Press
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
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.
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