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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15764
Title: Abnormal event detection in video using motion and appearance information
Authors: Menejes Palomino, Neptalí
Cámara Chávez, Guillermo
Keywords: Computer vision;Feature extraction;Information use;Motion analysis;Optical flows;Security systems;Abnormal event detections;Detection accuracy;Detection and localization;Motion information;Spatio temporal features;State-of-the-art methods;Video analysis;Video surveillance;Pattern recognition
Issue Date: 2018
Publisher: Springer Verlag
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042233597&doi=10.1007%2f978-3-319-75193-1_46&partnerID=40&md5=1a2b7d8eee873e88746bf326eb94785b
Abstract: This paper presents an approach for the detection and localization of abnormal events in pedestrian areas. The goal is to design a model to detect abnormal events in video sequences using motion and appearance information. Motion information is represented through the use of the velocity and acceleration of optical flow and the appearance information is represented by texture and optical flow gradient. Unlike literature methods, our proposed approach provides a general solution to detect both global and local abnormal events. Furthermore, in the detection stage, we propose a classification by local regions. Experimental results on UMN and UCSD datasets confirm that the detection accuracy of our method is comparable to state-of-the-art methods. © Springer International Publishing AG, part of Springer Nature 2018.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15764
ISBN: urn:isbn:9783319751924
ISSN: 3029743
Appears in Collections:Artículos - Ciencia de la computación

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