Extended Object Tracking Using Aspect Ratio

Extended object tracking (EOT) jointly estimates the object's kinematic state and extension including the shape, size, and orientation based on measurements from the sensor. For elliptical and rectangular objects, length and width are important parameters of the extension. However, directly modeling and estimation of the length and width may suffer from the uncertainties in resolution of the sensor and the observing angle and the distance between the sensor and object. The aspect ratio (ratio of length to width or width to length) is a key quantity for describing the shape, a constant for rigid body, and an inherent feature that can be directly used for object classification. Moreover, this ratio is insensitive to the above uncertainties. This paper focuses on modeling the extended object and utilizes the aspect ratio in the tracking approach. New dynamic and measurement models are proposed, and the aspect ratio is modeled by a gamma distribution. Then a variational Bayesian approach is derived, which gives estimates of the kinematic state and extension in an analytical way. The approach is evaluated by a simulated and three experimental data sets. Results show that utilizing the aspect ratio can improve the EOT performance in both single- and multiple-sensor cases, compared with the random matrix method and the parameterized elliptical object tracking method.