# Interaction-Aware Labeled Multi-Bernoulli Filter

arXiv: https://arxiv.org/abs/2204.08655 · HTML: https://mlsanity.com/paper/2204.08655/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 73 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Learning to Track with Object Permanence](https://mlsanity.com/paper/2103.14258/) (2021)
- [Can Deep Learning be Applied to Model-Based Multi-Object Tracking?](https://mlsanity.com/paper/2202.07909/) (2022)
- [Robust Distributed Fusion with Labeled Random Finite Sets](https://mlsanity.com/paper/1710.00501/) (2017)
- [Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection](https://mlsanity.com/paper/1603.08340/) (2016)
- [Multi-Sensor Control for Multi-Object Bayes Filters](https://mlsanity.com/paper/1702.05858/) (2017)
- [Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion](https://mlsanity.com/paper/1711.10783/) (2017)
- [Tracking as A Whole: Multi-Target Tracking by Modeling Group Behavior with Sequential Detection](https://mlsanity.com/paper/1904.12641/) (2019)
- [Multi-Bernoulli Sensor-Control via Minimization of Expected Estimation Errors](https://mlsanity.com/paper/1503.07276/) (2015)
- [Distributed Multi-sensor Multi-view Fusion based on Generalized Covariance Intersection](https://mlsanity.com/paper/1903.06985/) (2019)

## Built on by (most cited first, 1 of 1)

- [Track Initialization and Re-Identification for~3D Multi-View Multi-Object Tracking](https://mlsanity.com/paper/2405.18606/) (2024)

## Abstract (arXiv)

Tracking multiple objects through time is an important part of an intelligent transportation system. Random finite set (RFS)-based filters are one of the emerging techniques for tracking multiple objects. In multi-object tracking (MOT), a common assumption is that each object is moving independent of its surroundings. But in many real-world applications, target objects interact with one another and the environment. Such interactions, when considered for tracking, are usually modeled by an interactive motion model which is application specific. In this paper, we present a novel approach to incorporate target interactions within the prediction step of an RFS-based multi-target filter, i.e. labeled multi-Bernoulli (LMB) filter. The method has been developed for two practical applications of tracking a coordinated swarm and vehicles. The method has been tested for a complex vehicle tracking dataset and compared with the LMB filter through the OSPA and OSPA(2) metrics. The results demonstrate that the proposed interaction-aware method depicts considerable performance enhancement over the LMB filter in terms of the selected metrics.