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One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set of random variables, namely routine variables, into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly, we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods.

Self-localization is critical for many unmanned aerial vehicles (UAVs) tasks such as formation flight, path planning, and activity coordination. Traditionally, UAV can locate itself using GPS combined with some inertial sensors. However, due to the complex flight environment or failure of the GPS receiver, the UAV may lose its GPS signal and fail to locate itself, resulting in devastating consequence. In this paper, we will consider the problem of cooperative localization among multiple UAVs, in which the UAVs with failure of GPS receiver can help each other to locate themselves through mutual information exchanged based on the relative distance measurements. Specifically, we propose a dynamic Nonparametric Belief Propagation (dNBP) algorithm to calculate the posterior distribution of UAV's position conditioned on all observations made in the whole UAVs group. The dNBP is a natural combination of NBP with particle filtering, suitable for treating with the nonlinear model and highly non-Gaussian distributions arising in our application. Furthermore, dNBP provides the basis for distributed algorithm in which messages are exchanges between neighboring UAVs. Thus, the computational burden is distributed across UAVs. Simulations in Matlab environment show the effectiveness of our method.

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