Robust real time moving people detection in surveillance scenarios
EntityUAM. Departamento de Tecnología Electrónica y de las Comunicaciones
PublisherInstitute of Electrical and Electronics Engineers
10.1109/AVSS.2010.332010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010. IEEE 2010. 241-247
Funded byThis work has partially supported by the Cátedra UAMInfoglobal ("Nuevas tecnologías de vídeo aplicadas a sistemas de video-seguridad") and by the Spanish Government (TEC2007-65400 SemanticVideo).
NotePersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. García Martín, and J. M. Martínez, "Robust real time moving people detection in surveillance scenarios", in 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010, p. 241 - 247
Rights© 2010 IEEE
In this paper an improved real time algorithm for detecting pedestrians in surveillance video is proposed. The algorithm is based on people appearance and defines a person model as the union of four models of body parts. Firstly, motion segmentation is performed to detect moving pixels. Then, moving regions are extracted and tracked. Finally, the detected moving objects are classified as human or nonhuman objects. In order to test and validate the algorithm, we have developed a dataset containing annotated surveillance sequences of different complexity levels focused on the pedestrians detection. Experimental results over this dataset show that our approach performs considerably well at real time and even better than other real and non-real time approaches from the state of art.
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