# Extended Object Tracking and Classification based on Linear Splines

arXiv: https://arxiv.org/abs/2410.24183 · HTML: https://mlsanity.com/paper/2410.24183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Extended Object Tracking: Introduction, Overview and Applications](https://mlsanity.com/paper/1604.00970/) (2016)
- [Extended Object Tracking with Random Hypersurface Models](https://mlsanity.com/paper/1304.5084/) (2013)
- [Tracking the Orientation and Axes Lengths of an Elliptical Extended Object](https://mlsanity.com/paper/1805.03276/) (2018)
- [A Rao-Blackwellized Particle Filter for Superelliptical Extended Target Tracking](https://mlsanity.com/paper/2406.10389/) (2024)

## Abstract (arXiv)

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.