Scene understanding and task optimisation using multimodal imaging sensors and context: a real-time implementation Scene understanding and task optimisation using multimodal imaging sensors and context: a real-time implementation Scen

摘要

The aim of this paper is to describe the progress and results of an imaging system designed to optimise the performance of human operator tasks through exploitation of multimodal sensors and scene context. The performance of tasks such as surveillance, target detection and situational awareness is dependent on the scene content, the sensors available and the algorithms deployed. Intelligent analysis of the scene into contextual regions allows specific algorithms to be optimised and appropriate sensors to be selected, thereby increasing the performance of the operator's tasks. Context-specific algorithms, which will adapt as the scene changes, are required. In the case discussed in this paper, the contextual regions include road, sky and vegetation, and the dynamic detection of each region utilises different sensor modalities. The paper will describe the overall system concept and a real-time imaging demonstrator using GPUs, which will be used for future demonstrations of the context-specific processing. Simulations of the context-specific scene analysis will be described using sensor data from a vehicle in a rural environment. The performance of a motion detection system with and without context will also be illustrated using measured image data.