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.
Persia, F., D'Auria, D., Pilato, G. (2020). Fast Learning and Prediction of Event Sequences in a Robotic System. In Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020 (pp.447-452). IEEE [10.1109/IRC.2020.00085].
Fast Learning and Prediction of Event Sequences in a Robotic System
D'Auria D;
2020
Abstract
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.
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