A moving objects detection algorithm is proposed in order to improve the performance in presence of moving objects appearing close in a 2D image but with different distances from the observer. The method requires two distinct cameras with slight horizontal displacement, giving two video sequences. Frame difference is used to evidence the moving objects from the background in each video sequence. Then a disparity map is computed to measure the distance of each object. Finally, these data are merged by using a clustering algorithm giving the number, size and position of moving objects. Most of the processing can be implemented using cellular neural networks (CNN). We tested this method over several 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., Perfetti, R. (2006). Detection of moving objects in a binocular video sequence. In Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications (pp.281-285). NEW YORK : IEEE [10.1109/CNNA.2006.341645].
Detection of moving objects in a binocular video sequence
COSTANTINI, GIOVANNI;
2006-01-01
Abstract
A moving objects detection algorithm is proposed in order to improve the performance in presence of moving objects appearing close in a 2D image but with different distances from the observer. The method requires two distinct cameras with slight horizontal displacement, giving two video sequences. Frame difference is used to evidence the moving objects from the background in each video sequence. Then a disparity map is computed to measure the distance of each object. Finally, these data are merged by using a clustering algorithm giving the number, size and position of moving objects. Most of the processing can be implemented using cellular neural networks (CNN). We tested this method over several 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
