Sequential Monte Carlo Methods for Tracking and Inference in Intelligent Transportation Systems Lyudmila Mihaylova University of Sheffield Real time system face a number of challenges such as needs to deal with nonlinearities, constraints and cope with large volumes of data. Sequential Monte Carlo methods and Markov Chain Monte Carlo (MCMC) methods constitute a family of methods that are able to solve such problems. This talk will present recently developed SMC and MCMC algorithms for the purposes of tracking groups of pedestrians and vehicles. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians or 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 platoons of cars, 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. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy.