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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf Study, Virtual ClassroomVideo and Text Based

Courses and Certificate Fees

Fees InformationsCertificate AvailabilityCertificate Providing Authority
INR 20870yesChalmers University of Technology, Gothenburg

The Syllabus

  • 0.1 Course Introduction
  • 0.2 Pre-course Survey
  • 0.3 Discussion Forum Guidelines
  • 0.4 MATLAB

  • 1.1 Introduction to Multi-object Tracking
  • 1.2 Challenges in MOT
  • 1.3 Bayesian Filtering
  • 1.4 Kalman Filter Review
  • 1.5 Assumed Density Filtering

  • 2.1 Introduction to SOT in Clutter
  • 2.2 Motion and Measurement Models
  • 2.3 SOT Conceptual Solution
  • 2.4 Single-object Tracking Algorithms
  • Section Assignment 2
  • Section Assignment 2 (ungraded)
  • Handout, section 2

  • 3.1 Introduction to tracking n objects in clutter
  • 3.2 Modelling the measurements
  • 3.3 Estimating n object density
  • 3.4 Data association as an optimization problem
  • 3.5 n Object tracking algorithms
  • 3.6 Multi Hypothesis Tracker
  • Section Assignment 3 (ungraded)
  • Extra exercises
  • Handout, section 3

  • 4.1 Introduction
  • 4.2 Random Finite Sets
  • 4.3 Common Random Finite Sets
  • 4.4 Standard models in MOT
  • 4.5 Probability hypothesis density filtering
  • 4.6 Metrics in MOT
  • Section Assignment 4
  • Section Assignment 4 (ungraded)
  • Handout, section 4

  • 5.1 Introduction
  • 5.2 Modelling a changing number of objects
  • 5.3 Multi-Bernoulli Mixture filter
  • 5.4 Poisson Multi-Bernoulli Mixture filter
  • 5.5 MOT filter implementation
  • 5.6 Labels
  • 5.7 Summary
  • Section Assignment 5
  • Section Assignment 5 (ungraded)
  • Handout, section 5

  • Post-course survey

Instructors

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