title: A multilevel Mixture-of-Experts framework for pedestrian classification.
abstract: Notwithstanding many years of progress, pedestrian recognition is still a difficult but important problem. We present a novel multilevel Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification. On pose-level, shape cues based on Chamfer shape matching provide sample-dependent priors for a certain pedestrian view. On modality-level, we represent each data sample in terms of image intensity, (dense) depth, and (dense) flow. On feature-level, we consider histograms of oriented gradients (HOG) and local binary patterns (LBP). Multilayer perceptrons (MLP) and linear support vector machines (linSVM) are used as expert classifiers.
authors.affiliation: Environment Perception Department, Daimler AG Group Research & MCG Development, 89081 Ulm, Germany. markus.enzweiler@daimler.com
authors.forename: Markus
authors.initials: M
authors.lastname: Enzweiler
authors.forename: Dariu M
authors.initials: DM
authors.lastname: Gavrila
mesh_terms: D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine