Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

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. Illustration of Tracking Groups/Extended Objects with the Bayesian approach. The peaks of the posterior state probability density function (shown on the top) correspond to the two groups G1 and G2 (visualized at the bottom). Based on the peaks one can deduce where the positions of the groups are. •An overview is provided of key sequential Monte Carlo methods for group and extended object tracking.•Current achievements, trends and challenges are presented.•Efficient implementations of sequential Monte Carlo algorithms in distributed and parallel ways.