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Human detection based on a probabilistic assembly of robust part detectors

Krystian Mikolajczyk 1 Cordelia Schmid 2, * Andrew Zisserman 3 
* Corresponding author
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : We describe a novel method for human detection in single images which can detect full bodies as well as close-up views in the presence of clutter and occlusion. Humans are modeled as flexible assemblies of parts, and robust part detection is the key to the approach. The parts are represented by co-occurrences of local features which captures the spatial layout of the partrsquos appearance. Feature selection and the part detectors are learnt from training images using AdaBoost. The detection algorithm is very efficient as (i) all part detectors use the same initial features, (ii) a coarse-to-fine cascade approach is used for part detection, (iii) a part assembly strategy reduces the number of spurious detections and the search space. The results outperform existing human detectors.
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Submitted on : Monday, December 20, 2010 - 9:09:30 AM
Last modification on : Thursday, January 20, 2022 - 4:12:48 PM
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Krystian Mikolajczyk, Cordelia Schmid, Andrew Zisserman. Human detection based on a probabilistic assembly of robust part detectors. European Conference on Computer Vision (ECCV '04), May 2004, Prague, Czech Republic. pp.69--82, ⟨10.1007/978-3-540-24670-1_6⟩. ⟨inria-00548537⟩



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