An Improved B-spline Extended Object Tracking Model using the Iterative Closest Point Method

A star-convex shape based on Cartesian B-splines provides a good model for detailed extended target tracking, suited for, e.g., high resolution automotive sensors. Motivated by real-world sensor data from traffic scenarios, we present an extended object tracking filter that (i) solves the problem of bad object initialization for contour tracking of mixed-size vehicles in a range of common traffic scenarios; (ii) enables accurate tracking of objects such as motorcycles, that generates detections distributed on the surface, rather than on the contour. Our approach is based on star-convex Cartesian B-spline polynomials, iterative closest point (ICP) and the convex hull. In particular, we implement the ICP algorithm to find the translation and rotation of the contour that best fit the sensor point cloud. We show that, while the original B-spline filter with a "second-timestep-initialization-procedure" fails to robustly track the object, our approach performs on par to the original B-spline filter with ground truth initialization. Furthermore, for targets generating detections on the surface, we utilize the convex hull algorithm on the point cloud. We show that our algorithm successfully tracks the object, while the original B-spline filter fails to robustly track the contour of a motorcycle.