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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.

Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system's proper operation. A direct way to estimate the SOH is through the measurement of the battery's capacity; however, this measurement during the battery's operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery's operation. These indicators are extracted from the battery's voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery's capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice.

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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One of the fundamental requirements for visual surveillance with smart camera networks is the correct association of camera's observations with the tracks of objects under tracking. Most of the current systems work in a centralized manner in that the observations on all cameras need to be transmitted to a central server where some data association algorithm is running. Recently some works have been shown for distributed data association based solely on appearance observation. However, how to perform distributed association inference using both appearance and spatio-temporal information is still unclear. In this article, we present a novel method for estimating the posterior distribution of the label of each observation, indicating which of the objects it comes from, based on belief propagation between neighboring cameras. We develop distributed forward and backward inference algorithms for online and offline application, respectively, and further extend the algorithms to the case of unreliable detection. We also incorporate the proposed inference algorithms into distributed EM framework to simultaneously solve the problem of data association and appearance model learning in a completely distributed manner. The proposed method is verified on artificial data and on real world observations collected by a camera networks in an office building.

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