Actas de congresos
Abnormal Crowd Behavior Detection Based On Gaussian Mixture Model
Registro en:
978-3-319-48881-3; 978-3-319-48880-6
Computer Vision - Eccv 2016 Workshops, Pt Ii. Springer Int Publishing Ag, v. 9914, p. 668 - 675, 2016.
0302-9743
WOS:000389501700047
10.1007/978-3-319-48881-3_47
Autor
Rojas
Oscar Ernesto; Tozzi
Clesio Luis
Institución
Resumen
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. 9914 668 675 14th European Conference on Computer Vision (ECCV) OCT 08-16, 2016 Amsterdam, NETHERLANDS