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We introduce Spot as an accurate and efficient system for multi-entity device-free (DF) detection and tracking. Current state-of-the-art systems focused on the tracking of a single entity because of the intractability of the multi-entity case that leads to exponential complexity. Spot provides a novel cross-calibration technique that reduces the overhead of multiple entities calibration from exponential to linear. Spot also captures the spatial relations between the entities' poses into a probabilistic energy minimization framework via a conditional random field model. The designed energy minimization function is solved by a binary graph-cut algorithm. We evaluate our system using a typical testbed and show that Spot can achieve a multi-entity median tracking error of less than 1.44m. This corresponds to 108.33% enhancement in median distance error over the state-of-the-art DF localization systems, which can only track a single entity. In addition, Spot can estimate the number of entities correctly to within one difference error with 92% accuracy. This highlights that Spot achieves its goals of having an accurate and efficient software-only DF tracking solution of multiple entities in indoor environments.
Sabek et al. (Sat,) studied this question.