Group Object Tracking with Sequential Monte Carlo Methods Based on a Parameterised Likelihood Function Nikolay Petrov 1 , Lyudmila Mihaylova 1 , Amadou Gning 1 and Donka Angelova 2 1 Lancaster University, School of Computing and Communication Systems, UK 2 Bulgarian Academy of Sciences, Bulgaria Email: {n.petrov, mila.mihaylova, e.gning}@lancaster.ac.uk, donka@bas.bg Abstract. Group objects are characterised with multiple measurements originat- ing from different locations of the targets constituting the group objects. This pa- per presents a novel general Sequential Monte Carlo (SMC) approach for group object tracking applicable to various nonlinear problems. The novelty in this work is in the derivation of the likelihood function for nonlinear measurement func- tions, with sets of measurements belonging to a bounded spatial region. Simu- lation results are presented when a group of objects is surrounded by a circular region. Accurate estimation results are presented both for the group object kine- matic state and extent. 1 Motivation Group object tracking is concerned with finding the patterns of behaviour and tracks of a whole group instead of each object separately. The group of objects are generating mul- tiple measurements which origin is unknown. Different methods are proposed in the lit- erature for solving the problem of group object tracking, e.g., [KF09,Koc08,BFF + 10a]. Several approaches consist in estimating the extent of the group, e.g., with random matrices as suggested in [Koc08, BFF + 10b]. Other related works are [GS05, SH07b, SH07a, SBH06, BH09a, BH09b, NKPH10]. In general the measurement uncertainty can belong to a hypercube or to another spatial shape. In our approach, we consider the general case with a nonlinear measure- ment equation and measurements. The main contributions of the work is in the derived likelihood function based on a parameterised shape and in the developed Sequential Monte Carlo (SMC) filter for group objects. Then we propagate this spatial measure- ment uncertainty through the Bayesian estimation framework. In this work inspired by ideas from [GS05, GGMS05] and combining it with the ap- proach from [PMGA11] we developed a novel approach for group object tracking. The remaining part of this paper is structured as follows. Section 2 formulates the problem. Section 3 derives the measurement likelihood function. Section 4 presents simulation results about the performance of the proposed apporoach and finally Section 5 sum- marises the results.