![]() Together, physics-derived and human-derived fusion (PHF) enhances situation awareness, provides situation understanding, and affords situation assessment. For example, text data can establish that a pedestrian is crossing the road in a low-resolution video and/or the activity type is the object turning. The need for such methodology resides in answering user queries, linking information over different collections, and providing meaningful product reports. In this chapter we propose a novel framework to fuse video data with text data for enhanced simultaneous tracking and identification. Challenging scenarios where context can play a role includes: object labeling, track correlation/stitching through dropouts, and activity recognition. ![]() ![]() There exist many methods for object tracking and classification however, video analytics systems suffer from robust methods that perform well in all operating conditions (i.e., scale changes, occlusions, high signal-to-noise ratios, etc.). Information fusion consists of organizing a set of data for correlation in time, association over collections, and estimation in space. ![]()
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