This paper presents a technique that combines the occurrence of certain events, as observed by different sensors, in order to detect and classify objects. This technique explores the extent of dependence between features being observed by the sensors, and generates more informed probability distributions over the events. Provided some additional information about the features of the object, this fusion technique can outperform other existing decision level fusion approaches that may not take into account the relationship between different features.
|Original language||English (US)|
|Title of host publication||2018 26th European Signal Processing Conference, EUSIPCO 2018|
|Publisher||European Signal Processing Conference, EUSIPCO|
|Number of pages||5|
|State||Published - Nov 29 2018|
|Event||26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy|
Duration: Sep 3 2018 → Sep 7 2018
|Name||European Signal Processing Conference|
|Other||26th European Signal Processing Conference, EUSIPCO 2018|
|Period||9/3/18 → 9/7/18|
Bibliographical notePublisher Copyright:
© EURASIP 2018.
- Decision Level Fusion
- Event based Classification
- Sensor Fusion