Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/329834
Type: Congresso
Title: Abnormal Crowd Behavior Detection Based On Gaussian Mixture Model
Author: Rojas
Oscar Ernesto; Tozzi
Clesio Luis
Abstract: Many of the state-of-the-art approaches for automatic abnormal behavior detection in crowded scenes are based on complex models which require high processing time and several parameters to be adjusted. This paper presents a simple new approach that uses background subtraction algorithm and optical flow to encode the normal behavior pattern through a Gaussian Mixture Model (GMM). Abnormal behavior is detected comparing new samples against the mixture model. Experimental results on standards anomaly detection and localization benchmarks are presented and compared to other algorithms considering detection rate and processing time.
Subject: Anomaly Behavior Detection
Optical Flow
Crowded Scenes Analysis
Anomaly Localization
Editor: Springer Int Publishing AG
Cham
Rights: fechado
Identifier DOI: 10.1007/978-3-319-48881-3_47
Address: https://link.springer.com/chapter/10.1007/978-3-319-48881-3_47
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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