Improved scene understanding through semantic reasoning and online learning

Jakub J. Moskal; Mieczyslaw M. Kokar; Sydney Whittington

doi:10.1117/12.2618394

8 June 2022 Improved scene understanding through semantic reasoning and online learning

Jakub J. Moskal, Mieczyslaw M. Kokar, Sydney Whittington

Proceedings Volume 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI; 121220I (2022) https://doi.org/10.1117/12.2618394
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

With the number of sensors constantly increasing, there is a great need for automating the processing of sensor data in order to reduce cognitive load and response time for manned systems and enable greater autonomy in unmanned systems. It is anticipated that the unprecedented access to sensor data (both in volume and variety) will lead to reduced false alarm rates and increased probability of detection of threats and targets. Effectively, this capability will support situational awareness and facilitate mission success. However, current signal and image processing systems largely ignore the scene context which hinders their performance. In this paper, we describe a machine learning- and semantic reasoning-based system for target detection which incorporates the context. It combines the state-of-the-art image and signal processing capability with the leading-edge logic-based semantic reasoning technology. The main focus of this paper is on the value added by the semantic reasoning to machine learning.

Conference Presentation

Citation Download Citation

Jakub J. Moskal, Mieczyslaw M. Kokar, and Sydney Whittington "Improved scene understanding through semantic reasoning and online learning", Proc. SPIE 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, 121220I (8 June 2022); https://doi.org/10.1117/12.2618394

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