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Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011, Article ID 684819, 16 pages doi:10.1155/2011/684819 Research Article Contextual Information and Covariance Descriptors for People Surveillance: An Application for Safety of Construction Workers Giovanni Gualdi,1 Andrea Prati,2 and Rita Cucchiara1 1 2 DISMI, DII, University of Modena and Reggio Emilia, 41122 Modena, Italy University of Modena and Reggio Emilia, 42122 Reggio Emilia, Italy Correspondence should be addressed to Andrea Prati, andrea.prati@unimore.it Received 30 April 2010; Revised 7 October 2010; Accepted 10 December 2010 Academic Editor: Luigi Di Stefano Copyright © 2011 Giovanni Gualdi et al. This is an open access article distributed under. | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 684819 16 pages doi 10.1155 2011 684819 Research Article Contextual Information and Covariance Descriptors for People Surveillance An Application for Safety of Construction Workers Giovanni Gualdi 1 Andrea Prati 2 and Rita Cucchiara1 1DII University of Modena and Reggio Emilia 41122 Modena Italy 2DISMI University of Modena and Reggio Emilia 42122 Reggio Emilia Italy Correspondence should be addressed to Andrea Prati andrea.prati@unimore.it Received 30 April 2010 Revised 7 October 2010 Accepted 10 December 2010 Academic Editor Luigi Di Stefano Copyright 2011 Giovanni Gualdi et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. In computer science contextual information can be used both to reduce computations and to increase accuracy. This paper discusses how it can be exploited for people surveillance in very cluttered environments in terms of perspective i.e. weak scene calibration and appearance of the objects of interest i.e. relevance feedback on the training of a classifier . These techniques are applied to a pedestrian detector that uses a LogitBoost classifier appropriately modified to work with covariance descriptors which lie on Riemannian manifolds. On each detected pedestrian a similar classifier is employed to obtain a precise localization of the head. Two novelties on the algorithms are proposed in this case polar image transformations to better exploit the circular feature of the head appearance and multispectral image derivatives that catch not only luminance but also chrominance variations. The complete approach has been tested on the surveillance of a construction site to detect workers that do not wear the hard hat in such scenarios the complexity and dynamics are very high making .
