Extended Object Tracking: Introduction, Overview and Applications
It serves as a tutorial and review for researchers and practitioners in fields like robotics and surveillance, but is incremental as it synthesizes existing methods without introducing new ones.
This article provides an overview of extended object tracking, defining the problem and discussing modeling aspects, basic approaches like random matrix and Kalman filter, and methods for tracking multiple objects using RFS and Non-RFS techniques, concluding with applications in sensor technologies.
This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.