This paper addresses the problem of classifying actions performed by a human subject in a video sequence. A representation eigenspace approach based on the visual appearance is used to train the classifier. Before dimensionality reduction exploiting the PCA/LLE algorithms, a high dimensional description of each frame of the video sequence is constructed, based on foreground blob analysis. The classification task is performed by matching incrementally the reduced representation of the test image sequence against each of the learned ones, and accumulating matching scores until a decision is obtained; to this aim, two different metrics are introduced and evaluated. Experimental results demonstrate that the approach is accurate enough and feasible for behavior classification. Furthermore, we argue that the choice of both the feature descriptor and the metric for the matching score can dramatically influence the performance of the results.

Behavior monitoring through automatic analysis of video sequences / C. Colombo; D. Comanducci; A. Del Bimbo. - ELETTRONICO. - (2007), pp. 1-6. ((Intervento presentato al convegno ACM International Conference on Image and Video Retrieval CIVR 2007 tenutosi a Amsterdam, The Netherlands nel July 2007.

Behavior monitoring through automatic analysis of video sequences

COLOMBO, CARLO;COMANDUCCI, DARIO;DEL BIMBO, ALBERTO
2007

Abstract

This paper addresses the problem of classifying actions performed by a human subject in a video sequence. A representation eigenspace approach based on the visual appearance is used to train the classifier. Before dimensionality reduction exploiting the PCA/LLE algorithms, a high dimensional description of each frame of the video sequence is constructed, based on foreground blob analysis. The classification task is performed by matching incrementally the reduced representation of the test image sequence against each of the learned ones, and accumulating matching scores until a decision is obtained; to this aim, two different metrics are introduced and evaluated. Experimental results demonstrate that the approach is accurate enough and feasible for behavior classification. Furthermore, we argue that the choice of both the feature descriptor and the metric for the matching score can dramatically influence the performance of the results.
CIVR 2007
ACM International Conference on Image and Video Retrieval CIVR 2007
Amsterdam, The Netherlands
July 2007
C. Colombo; D. Comanducci; A. Del Bimbo
File in questo prodotto:
File Dimensione Formato  
civr07.pdf

Accesso chiuso

Tipologia: Pdf editoriale (Version of record)
Licenza: DRM non definito
Dimensione 318.8 kB
Formato Adobe PDF
318.8 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in FLORE 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: http://hdl.handle.net/2158/387835
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact