Distributed data association in smart camera networks

Distributed Bayesian Inference for Consistent Labeling of Tracked Objects in Nonoverlapping Camera Networks

“…This precludes the use of most existing distributed inference or optimization algorithms for traditional WSN and overlapping camera networks. In our recent works [2], based on the nonmissing detection assumption, we use a spatiotemporal tree to model the dependence of involved variables, and use belief propagation algorithm for calculating the posterior probability of labeling variable, which can be viewed as observation ownership in E-step of the distributed EM framework. Compared with traditional distributed EM, significant performance gain has been obtained by the effective using of spatiotemporal information.…”

“…The main limitations of the work in [2] are (i) the number of objects under tracking needs to be known beforehand and (ii) the appearance of a single object is assumed to follow a Gaussian distribution. In this paper, we propose a new distributed Bayesian inference framework for consistent labeling of the tracked objects in nonoverlapping camera networks, which nicely overcomes the above limitations.…”

“…In our method, under the same nonmissing detection assumption as in [2], the posterior distribution of each labeling variable conditioned on all appearance and spatiotemporal measurements made in the networks is calculated, based solely on local inference on each camera nodes and belief propagation between neighboring cameras. This is possible because the nonmissing detection assumption ensures that when the label of an observation made on a specific camera is inferred, all relevant information made by the networks have been summarized in the belief states of labeling variables corresponding to observations already generated on the camera's neighbors.…”

“…Unlike [2], we do not prespecify a fixed number of possible objects, that is, a fixed sampling space for each labeling variables. Instead, we allow each newly arriving observation to ignite a new possible object, or equivalently, to add a new element in the sampling space, as we notice the fact that each observation is either originated from a previously observed object or from a newly appeared one.…”

“…In addition, in this work, we discard the Gaussian assumption of object appearance used in [2]. Instead, we only assume that two observations are “similar” if they originate from the same object.…”