Integration of multisensor data for overcrowding estimation

Ottonello, C.; Peri, M.; Regazzoni, Carlo S.; Teseï, Alessandra

doi:10.1109/icsmc.1992.271529

Cited by 5 publications

(3 citation statements)

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“…BN tools have been used successfully for many military and civilian applications, particularly for decision making under uncertainty. Examples include medical diagnosis [26], forecasting [27], automated vision [28], sensor fusion [29], manufacturing control [30], and most recently in computer network security modeling [31], and combat aircraft identification [32]. The techniques proposed here are especially useful in the applications such as target identification for military defense where real-time requirement is critical.…”

Section: Discussionmentioning

confidence: 98%

Almost instant time inference for hybrid partially dynamic Bayesian networks

Chang

2007

IEEE Trans. Aerosp. Electron. Syst.

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A Bayesian network (BN) is a compact representation for probabilistic models and inference. They have been used successfully for many military and civilian applications. It is well known that, in general, the inference algorithms to compute the exact a posterior probability of a target node given observed evidence are either computationally infeasible for dense networks or impossible for general hybrid networks. In those cases, one either computes the approximate results using stochastic simulation methods or approximates the model using discretization or a Gaussian mixture model before applying an exact inference algorithm. This paper combines the concept of simulation and model approximation to propose an efficient algorithm for those cases. The main contribution here is a unified treatment of arbitrary (nonlinear non-Gaussian) hybrid (discrete and continuous) BN inference having both computation and accuracy scalability. The key idea is to precompile the high-dimensional hybrid distribution using a hypercube representation and apply it for both static and dynamic BN inference. Since the inference process essentially becomes a combination of table look-up and some simple operations, the method is shown to be extremely efficient. It can also be scaled to achieve any desirable accuracy given sufficient preprocessing time and memory for the cases where exact inference is not possible.

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Section: Discussionmentioning

confidence: 98%

Almost instant time inference for hybrid partially dynamic Bayesian networks

Chang

2007

IEEE Trans. Aerosp. Electron. Syst.

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“…Bayesian networks and bayesian inference have proven to be useful methods for modeling complex systems, enabling predictions and decision-making in medical diagnosis, weather forecasting, sensor fusion, and gene regulatory networks. [1][2][3][4][5] A Bayesian network is a probabilistic graphical model that represents the conditional dependence of stochastic variables on the updated data using a directed acyclic graph. 6 It provides an efficient framework for probabilistic inference of posterior probabilities based on real-world data.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Bayesian networks and Bayesian inference using a Cu_0.1Te_0.9/HfO₂/Pt threshold switching memristor

Baek,

Lee,

Jang

et al. 2024

Nanoscale Adv.

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“…In the operation phase, the detected features are integrated across time through an extended Kalman filter to improve the results. Although they had shown improvement over a competitive approach based on belief Bayesian networks [22], their method was mostly focused on indoor scenes with a limited number of people (up to around 30). Furthermore, the offline training procedure is adjusted to a given camera setup and scenario, and changes require a new training phase.…”

Section: Pixel-level Analysismentioning

confidence: 99%