Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking
Mihaylova, Lyudmila; Carmi, Avishy Y.; Septier, François; Gning, Amadou; Pang, Sze Kim; Godsill, Simon
Avishy Y. Carmi
Sze Kim Pang
This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced.
|Journal Article Type||Article|
|Journal||Digital Signal Processing|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Mihaylova, L., Carmi, A. Y., Septier, F., Gning, A., Pang, S. K., & Godsill, S. (2014). Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking. Digital Signal Processing, 25, 1-16. https://doi.org/10.1016/j.dsp.2013.11.006|
|Keywords||Signal Processing; Electrical and Electronic Engineering|
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
You might also like
A box particle filter method for tracking multiple extended objects
Analysis of the EPSRC Principles of Robotics in regard to key research topics
Autonomous crowds tracking with box particle filtering and convolution particle filtering
Box-particle probability hypothesis density filtering