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Pedestrian Detection by Probabilistic Component Assembly

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Pattern Recognition (DAGM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5748))

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Abstract

We present a novel pedestrian detection system based on probabilistic component assembly. A part-based model is proposed which uses three parts consisting of head-shoulder, torso and legs of a pedestrian. Components are detected using histograms of oriented gradients and Support Vector Machines (SVM). Optimal features are selected from a large feature pool by boosting techniques, in order to calculate a compact representation suitable for SVM. A Bayesian approach is used for the component grouping, consisting of an appearance model and a spatial model. The probabilistic grouping integrates the results, scale and position of the components. To distinguish both classes, pedestrian and non-pedestrian, a spatial model is trained for each class. Below miss rates of 8% our approach outperforms state of the art detectors. Above, performance is similar.

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Rapus, M., Munder, S., Baratoff, G., Denzler, J. (2009). Pedestrian Detection by Probabilistic Component Assembly. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_10

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  • DOI: https://doi.org/10.1007/978-3-642-03798-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03797-9

  • Online ISBN: 978-3-642-03798-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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