People detection in surveillance: Classification and evaluation
EntityUAM. Departamento de Tecnología Electrónica y de las Comunicaciones
PublisherInstitution of Engineering and Technology
10.1049/iet-cvi.2014.0148IET Computer Vision 9.5 (2015): 779 – 788
ISSN1751-9632 (print); 1751-9640 (online)
Funded byThis work has been partially supported by the Spanish Government (TEC2011-25995 EventVideo).
ProjectGobierno de España. TEC2011-25995
SubjectsImage classification; Image sequences; Object detection; Video surveillance; Telecomunicaciones
NoteThis paper is a postprint of a paper submitted to and accepted for publication in IET Computer Vision and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library and at IEEE Xplore.
Rights© The Institution of Engineering and Technology 2015
Nowadays, people detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. The main objective of this study is to give a comprehensive and extensive evaluation of the state of the art of people detection regardless of the final surveillance application. For this reason, first, the different processing tasks involved in the automatic people detection in video sequences have been defined, then a proper classification of the state of the art of people detection has been made according to the two most critical tasks, object detection and person model, that are needed in every detection approach. Finally, experiments have been performed on an extensive dataset with different approaches that completely cover the proposed classification and support the conclusions drawn from the state of the art.
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