Extended Object Tracking: Introduction, Overview and Applications

TL;DR

This paper provides a comprehensive overview of extended object tracking, discussing models, approaches, multi-object tracking, and applications across various sensor types, serving as a foundational resource for researchers and practitioners.

Contribution

It offers a detailed synthesis of current research, clarifies the problem definition, compares key tracking approaches, and highlights diverse real-world applications.

Findings

Comparison of random matrix and Kalman filter approaches

Discussion on multi-object tracking with RFS and Non-RFS methods

Summary of applications across camera, radar, lidar, and RGB-D sensors

Abstract

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.