Generative Graphical Models for Maneuvering Object Tracking and Dynamics Analysis

We study the challenging problem of maneuvering object tracking with unknown dynamics, i.e., forces or torque. We investigate the underlying causes of object kinematics, and propose a generative model approach that encodes the Newtonian dynamics for a rigid body by relating forces and torques with object´s kinematics in a graphical model. This model also accommodates the physical constraints between maneuvering dynamics and object kinematics in a probabilistic form, allowing more accurate and efficient object tracking. Additionally, we develop a sequential Monte Carlo inference algorithm that is embedded with Markov Chain Monte Carlo (MCMC) steps to rejuvenate the path of particles. The proposed algorithm can estimate both maneuvering dynamics and object kinematics simultaneously. The experiments performed on both simulated and real-world data of ground vehicles show the robustness and effectiveness of the proposed graphical model-based approach along with the sampling-based inference algorithm.