  Roadside perception is a key component in intelligent transportation systems.
In this paper, we present a novel three-dimensional (3D) extended object
tracking (EOT) method, which simultaneously estimates the object kinematics and
extent state, in roadside perception using both the radar and camera data.
Because of the influence of sensor viewing angle and limited angle resolution,
radar measurements from objects are sparse and non-uniformly distributed,
leading to inaccuracies in object extent and position estimation. To address
this problem, we present a novel spherical Gaussian function weighted Gaussian
mixture model. This model assumes that radar measurements originate from a
series of probabilistic weighted radar reflectors on the vehicle's extent.
Additionally, we utilize visual detection of vehicle keypoints to provide
additional information on the positions of radar reflectors. Since keypoints
may not always correspond to radar reflectors, we propose an elastic skeleton
fusion mechanism, which constructs a virtual force to establish the
relationship between the radar reflectors on the vehicle and its extent.
Furthermore, to better describe the kinematic state of the vehicle and
constrain its extent state, we develop a new 3D constant turn rate and velocity
motion model, considering the complex 3D motion of the vehicle relative to the
roadside sensor. Finally, we apply variational Bayesian approximation to the
intractable measurement update step to enable recursive Bayesian estimation of
the object's state. Simulation results using the Carla simulator and
experimental results on the nuScenes dataset demonstrate the effectiveness and
superiority of the proposed method in comparison to several state-of-the-art 3D
EOT methods.
