Semantically Enhanced UAVs to Increase the Aerial Scene Understanding
| Autor: | Alessia Saggese, Mario Vento, Vincenzo Loia, Sabrina Senatore, Danilo Cavaliere |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Context (language use) Mobile camera 02 engineering and technology video tracking 0502 economics and business 0202 electrical engineering electronic engineering information engineering Image noise unmanned aerial vehicles (UAVs) Computer vision Electrical and Electronic Engineering 050210 logistics & transportation business.industry 05 social sciences Representation (systemics) Object (computer science) Object detection Computer Science Applications semantic Web situation awareness Software Control and Systems Engineering Human-Computer Interaction Computer Vision and Pattern Recognition Video tracking Eye tracking 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems. 49:555-567 |
| ISSN: | 2168-2232 2168-2216 |
| DOI: | 10.1109/tsmc.2017.2757462 |
| Popis: | Visual tracking supported by unmanned aerial vehicles (UAVs) has generated a lot of interest in recent years, especially in application domains such as surveillance, search for missing persons and traffic monitoring. The major challenges in visual tracking with small UAVs arise in the form of target representation, target appearance change, target detection and localization in real time computation. Reliable target detection depends on factors such as occlusions, image noise, illumination and pose changes, or image blur that may compromise the object labeling. To mitigate these issues, this paper proposes a hybrid solution: along with the tracked objects, scenes are completely depicted by adding contextual information, i.e., data describing places, natural features, or in general points of interest. Each scenario indeed is semantically described by ontological statements that define the context and then, by inference, support the object tracking task in the object identification and labeling. The synergy between the tracking methods and semantic modeling can bridge the object labeling gap, enhancing the scene understanding and awareness when alarming situations are discovered. Experimental results are promising and confirm the applicability of the proposed framework in supporting drones in object identification and critical situation detection tasks. |
| Databáze: | OpenAIRE |
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