Ιδρυματικό Αποθετήριο [SANDBOX]
Πολυτεχνείο Κρήτης
EN  |  EL

Αναζήτηση

Πλοήγηση

Ο Χώρος μου

Automated spatiotemporal scaling for video generalization

Partsinevelos Panagiotis, Stefanidis, A, Agouris, P.

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/FDA2A5F1-3F69-4CF7-8D59-6AE0E4AF17BE
Έτος 2001
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά Partsinevelos P., A. Stefanidis & P. Agouris , Automated Spatiotemporal Scaling for Video Generalization, IEEE-ICIP 2001, Thessaloniki, Greece, 2001, vol.1 ,p.177 - 180, DOI:10.1109/ICIP.2001.958982 https://doi.org/10.1109/ICIP.2001.958982
Εμφανίζεται στις Συλλογές

Περίληψη

We present a technique for the summarization and spatiotemporal scaling of video content. A self organizing map (SOM) neural network can be used to acquire a rough generalization of the spatiotemporal trajectories of moving objects, in the form of few selected nodes along these trajectories. We introduce a hybrid technique, combining SOM with geometric analysis to properly densify these nodes, to better represent the spatiotemporal behavior of objects. This allows us to bypass problems inherently associated with parameter selection in SOM. We also demonstrate how spatiotemporal scaling supports the analysis of behavioral patterns. The paper shows that our novel technique is a powerful tool for the extraction of generalized information from complex trajectories, displaying high invariance to noise and information gaps in the video stream. Experimental results demonstrate the accuracy potential of our generalization technique

Υπηρεσίες

Στατιστικά