Multi-modal Video Fusion for Context-aided Tracking

Abstract

There have been many advances in image fusion to support multi-modal, multi-perspective, and multi-focal day-night robust surveillance. Contextual analysis of multi-sensor exploitation supports many information fusion systems for target tracking, situation awareness, and scene understanding. This paper highlights an example for electro-optical and infrared image fusion analysis and colorization in support of simultaneous tracking and identification for physics-based and human-derived information fusion (PHIF). The results provide an ongoing discussion and analysis of the importance of context as both a source to guide information fusion systems (e.g., sensor mode selection), but also the need to be able to provide users with robust systems over context changes (e.g., variations in illumination).