Browse by Stream
- Engineering and Architecture
Exams
Colleges
Predictors
- Management and Business Administration
Colleges & Courses
Predictors
- Medicine and Allied Sciences
Colleges
Predictors
- Law
Colleges
Predictors & E-Books
- Animation and Design
Predictors & Articles
Colleges
- Media, Mass Communication and Journalism
Colleges
- Finance & Accounts
Resources
Top Courses & Careers
- Computer Application and IT
Colleges
- Pharmacy
Colleges
- Hospitality and Tourism
Colleges
Resources
Diploma Colleges
- Competition
Resources
Upcoming Events
- School
Exams
Top Schools
Products & Resources
- Study Abroad
Colleges
Top Countries
- Arts, Commerce & Sciences
Colleges
Upcoming Events
- Learn
Engineering Preparation
Medical Preparation
Online Courses
- Online Courses and Certifications
Top Streams
Specializations
Resources
Search Colleges, Exams, Schools & more
Login
Expert
Online
10 Weeks
Free Interested in this College?
Get updates on Eligibility, Admission, Placements Fees Structure
CompareGet updates on Eligibility, Admission, Placements Fees Structure
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
Lennart Svensson
Professor
Chalmers University of Technology, Gothenburg
Yuxuan Xia
Student
Chalmers University of Technology, Gothenburg
Karl Granstrom
Perception specialist
Freelancer
Similar Courses
National Institute of Electronics and Information Technology, Gorakhpur
Months 41.20 K
Indian Institute of Technology Guwahati via Coursera
Months 61.13 L
The University of Leeds via Futurelearn
Months 2Free
More Courses by Chalmers University of Technology, Gothenburg
Chalmers University of Technology, Gothenburg via Edx
Months 8Free
Chalmers University of Technology, Gothenburg via Edx
Months 9Free
Chalmers University of Technology, Gothenburg via Edx
Months 8Free
Chalmers University of Technology, Gothenburg via Edx
Months 7Free
Chalmers University of Technology, Gothenburg via Edx
Months 7Free
Chalmers University of Technology, Gothenburg via Edx
Months 8Free
Chalmers University of Technology, Gothenburg via Edx
Months 8Free
Chalmers University of Technology, Gothenburg via Edx
Months 9Free
Explore on Careers360
Knowledge Boosters
Ask
Question
Student Community: Where Questions Find Answers
Back to top