A mixed generative-discriminative framework for pedestrian classification

Markus Enzweiler, Dariu M. Gavrila
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  
This paper presents a novel approach to pedestrian classification which involves utilizing the synthesized virtual samples of a learned generative model to enhance the classification performance of a discriminative model. Our generative model captures prior knowledge about the pedestrian class in terms of a number of probabilistic shape and texture models, each attuned to a particular pedestrian pose. Active learning provides the link between the generative and discriminative model, in the
more » ... that the former is selectively sampled such that the training process is guided towards the most informative samples of the latter. In large-scale experiments on real-world datasets of tens of thousands of samples, we demonstrate a significant improvement in classification performance of the combined generative-discriminative approach over the discriminative-only approach (the latter exemplified by a neural network with local receptive fields and a support vector machine using Haar wavelet features).
doi:10.1109/cvpr.2008.4587592 dblp:conf/cvpr/EnzweilerG08 fatcat:t2s6tmys2jdhtezqsnrt3oc2ru