Comparison of accumulative computation with traditional optical flow

Authors:
Antonio Fernández-Caballero;Rafael Pérez-Jiménez;Miguel A. Fernández;María T. López
Affiliations:
Departmento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Escuela Politécnica Superior de Albacete, Albacete, Spain;Departmento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Escuela Politécnica Superior de Albacete, Albacete, Spain;Departmento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Escuela Politécnica Superior de Albacete, Albacete, Spain;Departmento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Escuela Politécnica Superior de Albacete, Albacete, Spain
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Year:
2007

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Abstract

Segmentation from optical flow calculation is nowadays a well-known technique for further labeling and tracking of moving objects in video streams. A likely classification of algorithms to obtain optical flow based on the intensity of the pixels in an image is in (a) differential or gradient-based methods and (b) block correlation or block matching methods. In this article, we are going to carry out a qualitative comparison of three well-known algorithms (two differential ones and a correlation one). We will do so by means of the optical flow obtaining method based on accumulated image differences known as accumulative computation.