In this paper we propose an algorithm that is able to detect moving objects, returning the number of found objects, together with their position, shape, and approximate distance. The system is based on two cameras, which are supposed to be fixed, a digital processor, and two analog chips, which perform data analysis. The use of a couple of cameras improves the performance in comparison with systems with only one camera, because it can exploit the availability of two images from two different points of view in order to get information on the distance of the objects from the two cameras, in the same way as the human eye does with its so called "binocular vision". We tested our method over several video sequences, both indoor and outdoor. Experimental results show a significantly improved discrimination when multiple objects are moving at different distances. Moreover, the use of stereo images can be exploited to reduce noise, improving performances for clustering.

Costantini, G., Casali, D., Carota, M., Perfetti, R. (2007). A CNN-based algorithm for moving object detection in stereovision applications. In 2007 EUROPEAN CONFERENCE ON CIRCUIT THEORY AND DESIGN, VOLS 1-3 (pp.500-503). NEW YORK : IEEE [10.1109/ECCTD.2007.4529642].

A CNN-based algorithm for moving object detection in stereovision applications

COSTANTINI, GIOVANNI;
2007-01-01

Abstract

In this paper we propose an algorithm that is able to detect moving objects, returning the number of found objects, together with their position, shape, and approximate distance. The system is based on two cameras, which are supposed to be fixed, a digital processor, and two analog chips, which perform data analysis. The use of a couple of cameras improves the performance in comparison with systems with only one camera, because it can exploit the availability of two images from two different points of view in order to get information on the distance of the objects from the two cameras, in the same way as the human eye does with its so called "binocular vision". We tested our method over several video sequences, both indoor and outdoor. Experimental results show a significantly improved discrimination when multiple objects are moving at different distances. Moreover, the use of stereo images can be exploited to reduce noise, improving performances for clustering.
European Conference on Circuit Theory and Design 2007, ECCTD 2007
Seville
AUG 26-30, 2007
Universidad de Sevilla;Institute of Electrical and Electronics Engineering (IEEE);IEEE Circuits and Systems Society;European Circuit Society (ECS);Ministerio de Educacion y Ciencia
Rilevanza internazionale
Settore ING-IND/31 - Elettrotecnica
English
Binocular vision; Cameras; Detector circuits; Image enhancement; Imaging techniques; Photography; Statistical methods; Tracking (position); Video recording; Analog chips; Circuit theory and design; Data analysis; Digital processors; European; Human eyes; Moving objects; Moving-object detection; Multiple objects; One camera; Stereo imaging; Stereo-vision; Video sequencing; Circuit theory
Intervento a convegno
Costantini, G., Casali, D., Carota, M., Perfetti, R. (2007). A CNN-based algorithm for moving object detection in stereovision applications. In 2007 EUROPEAN CONFERENCE ON CIRCUIT THEORY AND DESIGN, VOLS 1-3 (pp.500-503). NEW YORK : IEEE [10.1109/ECCTD.2007.4529642].
Costantini, G; Casali, D; Carota, M; Perfetti, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/52639
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