Om kursen
The material includes the following content:
An introduction to single-object tracking including Kalman filters, probabilistic data association and nearest neighbor filtering.
Organization and examination
See course homepage on edX for details.
Prerequisites
We recommend that you have taken a course that teaches Kalman filtering, such as SSY345 - Sensor fusion and nonlinear filtering.
An introduction to single-object tracking including Kalman filters, probabilistic data association and nearest neighbor filtering.
- An overview of vector-based multi-object tracking, including joint probabilistic data association (JPDA), global nearest neighbor (GNN) and multi-hypothesis tracking (MHT).
- An description of the main components related to random finite sets (RFSs), including RFS densities, RFS models, PHD filters and performance metrics on RFSs.
- An introduction to the state-of-the-art algorithms that make use of conjugate priors, with an emphasis on Poisson multi-Bernoulli mixture (PMBM) filters and labelled multi-Bernoulli filters.
Organization and examination
See course homepage on edX for details.
Prerequisites
We recommend that you have taken a course that teaches Kalman filtering, such as SSY345 - Sensor fusion and nonlinear filtering.
Mer information
Lennart Svensson
Telephone: 031-772 1777
E-mail: lennart.svensson@chalmers.se
Föreläsare
Lennart Svensson
E-mail: lennart.svensson@chalmers.se
