The most important requirements for a video surveillance system are efficiency and effectiveness. In fact, it has to be fast in detecting a potentially dangerous event in real time, but it has also not to miss any of them. However, it would be even better if a system could detect dangerous events even before they actually occur. For that reason, in this paper we propose a very fast approach for learning and predicting event sequences in a surveillance context, that can also be applied to a robotic platform for improving the whole monitoring process. Preliminary experiments confirm that the proposed approach is very promising.
|Titolo:||Fast Learning and Prediction of Event Sequences in a Robotic System|
PERSIA, Fabio (Corresponding)
|Data di pubblicazione:||2020|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|