We present a method to model and classify trajectory data that come from surveillance videos. Observations of the locations of moving entities are used to estimate their expected velocity in the scene. Such estimation is performed by a Gaussian process regression that enables to approximate probabilistically the expected velocity of entities given some observed evidence in the scene. Subsequently, regions where estimations have high certainty are decomposed into zones by superpixel segmentation. Each zone represents a region where motions of entities can be explained by a quasilinear dynamical model. We evaluated the proposed method with two datasets and confirmed its reliability for characterizing and classifying trajectories.

Modeling and classification of trajectories based on a Gaussian process decomposition into discrete components

CAMPO CAICEDO, DAMIAN ANDRES;BAYDOUN, MOHAMAD;Marcenaro, Lucio;Regazzoni, Carlo S.
2017-01-01

Abstract

We present a method to model and classify trajectory data that come from surveillance videos. Observations of the locations of moving entities are used to estimate their expected velocity in the scene. Such estimation is performed by a Gaussian process regression that enables to approximate probabilistically the expected velocity of entities given some observed evidence in the scene. Subsequently, regions where estimations have high certainty are decomposed into zones by superpixel segmentation. Each zone represents a region where motions of entities can be explained by a quasilinear dynamical model. We evaluated the proposed method with two datasets and confirmed its reliability for characterizing and classifying trajectories.
2017
9781538629390
File in questo prodotto:
File Dimensione Formato  
Modeling and classification of trajectories based on a Gaussian process decomposition into discrete components.pdf

accesso chiuso

Descrizione: Articolo principale
Tipologia: Documento in versione editoriale
Dimensione 825.05 kB
Formato Adobe PDF
825.05 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/886621
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 1
social impact