# Visual Multiple-Object Tracking for Unknown Clutter Rate

## TL;DR

This paper introduces a multi-object tracking algorithm capable of handling unknown false measurement rates by combining robust multi-Bernoulli filtering for clutter estimation with generalized labeled multi-Bernoulli filtering for target tracking, validated on real videos.

## Contribution

The paper presents a novel multi-object tracking method that effectively manages unknown clutter rates using combined filtering techniques, enhancing real-world tracking reliability.

## Key findings

- Effective clutter estimation in real-world scenarios
- Improved tracking accuracy with unknown false measurement rates
- Validated performance on real video data

## Abstract

In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views. In this paper we are interested in designing a multi-object tracking algorithm that handles unknown false measurement rate. Recently proposed robust multi-Bernoulli filter is employed for clutter estimation while generalized labeled multi-Bernoulli filter is considered for target tracking. Performance evaluation with real videos demonstrates the effectiveness of the tracking algorithm for real-world scenarios.