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Track-Before-Detect Using Expectation Maximisation [electronic resource] : The Histogram Probabilistic Multi-hypothesis Tracker: Theory and Applications / by Samuel J. Davey, Han X. Gaetjens.

By: Davey, Samuel J [author.].
Contributor(s): Gaetjens, Han X [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Signals and Communication Technology: Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XXI, 352 p. 127 illus., 117 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811075933.Subject(s): Signal processing | Computer vision | Mathematics—Data processing | Computational intelligence | Signal, Speech and Image Processing | Computer Vision | Computational Science and Engineering | Computational IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
Foreword -- Introduction -- Idealised Track-Before-Detect -- Point Measurement Probabilistic Multi-Hypothesis Tracking -- Histogram ProbabilisticMulti-Hypothesis Tracking -- Implementation Considerations -- Poisson Scattering Field -- Non-linear Non-Gaussian Targets -- Adaptive Appearance Models -- H-PMHT with Attribute Data -- Radar and Sonar Track-Before-Detect -- Tracking in Full Motion Video -- Related Methods -- Summary and Prognostications.
In: Springer Nature eBookSummary: This book offers a detailed description of the histogram probabilistic multi-hypothesis tracker (H-PMHT), providing an accessible and intuitive introduction to the mathematical mechanics of H-PMHT as well as a definitive reference source for the existing literature on the method. Beginning with basic concepts, the authors then move on to address extensions of the method to a broad class of tracking problems. The latter chapters present applications using recorded data from experimental radar, sonar and video sensor systems. The book is supplemented with software that both furthers readers’ understanding and acts as a toolkit for those who wish to apply the methods to their own problems.
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Foreword -- Introduction -- Idealised Track-Before-Detect -- Point Measurement Probabilistic Multi-Hypothesis Tracking -- Histogram ProbabilisticMulti-Hypothesis Tracking -- Implementation Considerations -- Poisson Scattering Field -- Non-linear Non-Gaussian Targets -- Adaptive Appearance Models -- H-PMHT with Attribute Data -- Radar and Sonar Track-Before-Detect -- Tracking in Full Motion Video -- Related Methods -- Summary and Prognostications.

This book offers a detailed description of the histogram probabilistic multi-hypothesis tracker (H-PMHT), providing an accessible and intuitive introduction to the mathematical mechanics of H-PMHT as well as a definitive reference source for the existing literature on the method. Beginning with basic concepts, the authors then move on to address extensions of the method to a broad class of tracking problems. The latter chapters present applications using recorded data from experimental radar, sonar and video sensor systems. The book is supplemented with software that both furthers readers’ understanding and acts as a toolkit for those who wish to apply the methods to their own problems.

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