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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 20870 | yes | Chalmers 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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