Track uncertainty resolution through confirmation of identity using correlation filters

Critical to a large portion of mission scenarios within the intelligence, surveillance, and reconnaissance (ISR) sensor community is the challenge to ensure designated targets of interest are reliably tracked in dynamic environments. Current generation trackers frequently loose track when targets become temporarily obscured, shadowed, or is in close proximity to other objects. In this paper we propose and demonstrate a generic confirmation of identity module that is based on the Distance Classifier Correlation Filter (DCCF) and is applicable to a variety of tracking technologies. The prevailing idea of this technique is that during a trackers valid track phase, learning exemplars are provided to a filter building process and templates of the tracked targets are created real-time online. Differences in orientation are handled through the creation of synthetic views using real target views and image warping techniques. After obscuration and/or during periods of track ambiguity, each new candidate track is matched against the prior valid track(s) using DCCF matching to resolve uncertainty.