# A Multi-Object Tracking Method with an Unscented Kalman Filter on a Lie Group Manifold

## TL;DR

This paper introduces a new multi-object tracking method using an unscented Kalman filter on a Lie group to improve accuracy in challenging scenarios.

## Contribution

The novel contribution is a lightweight MOT method, LUKF-Track, using a UKF on a Lie group for better tracking in complex conditions.

## Key findings

- LUKF-Track achieves state-of-the-art results on MOT17, MOT20, and DanceTrack benchmarks.
- The method improves tracking accuracy in scenarios with nonlinear motion and heavy occlusions.
- It uses a motion model with a UKF on a Lie group for object state prediction and association.

## Abstract

Multi-object tracking (MOT) has attracted increasing attention and achieved remarkable progress. However, accurately tracking objects with homogeneous appearance, heterogeneous motion, and heavy occlusion remains a challenge because of two problems: (1) missing association due to recognizing an object as background and (2) false prediction caused by the predominant utilization of linear motion models and the insufficient discriminability of object appearance representations. To address these challenges, this paper proposes a lightweight, generic, and appearance-independent MOT method with an unscented Kalman filter (UKF) on a Lie group called LUKF-Track. The method utilizes detection boxes across the entire range of scores in data association and matches objects across frames by employing a motion model, where the propagation and prediction of object states are formulated using a UKF on the Lie group. LUKF-Track achieves state-of-the-art results on three public benchmarks, MOT17, MOT20, and DanceTrack, which are characterized by highly nonlinear object motion and severe occlusions.