Extended Object Tracking and Classification based on Linear Splines

TL;DR

This paper presents a novel linear spline-based framework for extended object tracking and classification, capable of representing complex object contours and integrating shape and kinematic estimation within a unified probabilistic approach.

Contribution

It introduces a new linear spline model for complex object contours, deriving exact and approximate likelihoods, and combines shape and kinematic estimation with implicit classification.

Findings

Outperforms existing extended object estimators in numerical tests

Effectively models complex object contours with linear splines

Integrates shape classification within the tracking process

Abstract

This paper introduces a framework based on linear splines for 2-dimensional extended object tracking and classification. Unlike state of the art models, linear splines allow to represent extended objects whose contour is an arbitrarily complex curve. An exact likelihood is derived for the case in which noisy measurements can be scattered from any point on the contour of the extended object, while an approximate Monte Carlo likelihood is provided for the case wherein scattering points can be anywhere, i.e. inside or on the contour, on the object surface. Exploiting such likelihood to measure how well the observed data fit a given shape, a suitable estimator is developed. The proposed estimator models the extended object in terms of a kinematic state, providing object position and orientation, along with a shape vector, characterizing object contour and surface. The kinematic state is estimated via a nonlinear Kalman filter, while the shape vector is estimated via a Bayesian classifier so that classification is implicitly solved during shape estimation. Numerical experiments are provided to assess, compared to state of the art extended object estimators, the effectiveness of the proposed one.