Multimodal Probabilistic Person Tracking and Identification in Smart Spaces

Abstract

In this thesis, a new methodology is introduced for the multimodal tracking and identification of multiple persons by seeking and integrating reliable ID cues whenever they become observable. The method opportunistically integrates person-specific identification cues that can only sparsely be observed for each person over time and keeps track of the location of identified persons while ID cues are not available

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