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Real-Time Sensor Management Strategies for Multi-Object Tracking

Abstract : Modern surveillance systems must coordinate their observation strategies to enhance the information obtained by their future measurements in order to accurately estimate the states of objects of interest (location, velocity, appearance, etc). Therefore, adaptive sensor management consists of determining sensor measurement strategies that exploit a priori information in order to determine current sensing actions. One of the most challenging applications of sensor management is the multi-object tracking, which refers to the problem of jointly estimating the number of objects and their states or trajectories from noisy sensor measurements. This thesis focuses on real-time sensor management strategies formulated in the POMDP framework to address the multi-object tracking problem within the LRFS approach. The first key contribution is the rigorous theoretical formulation of the mono-sensor LPHD filter with its Gaussian-mixture implementation. The second contribution is the extension of the mono-sensor LPHD filter for superpositional sensors, resulting in the theoretical formulation of the multi-sensor LPHD filter. The third contribution is the development of the Expected Risk Reduction (ERR) sensor management method based on the minimization of the Bayes risk and formulated in the POMDP and LRFS framework. Additionally, analyses and simulations of the existing sensor management approaches for multi-object tracking, such as Task-based, Information-theoretic, and Risk-based sensor management, are provided.
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Submitted on : Tuesday, May 14, 2019 - 6:16:12 PM
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  • HAL Id : tel-02129308, version 1



Marcos Eduardo Gomes Borges. Real-Time Sensor Management Strategies for Multi-Object Tracking. Automatic Control Engineering. Ecole Centrale de Lille, 2018. English. ⟨NNT : 2018ECLI0019⟩. ⟨tel-02129308⟩



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