# A New Architecture for Neural Enhanced Multiobject Tracking

arXiv: https://arxiv.org/abs/2410.06294 · HTML: https://mlsanity.com/paper/2410.06294/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 27 of 27)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking](https://mlsanity.com/paper/2303.11301/) (2023)
- [Learning a Neural Solver for Multiple Object Tracking](https://mlsanity.com/paper/1912.07515/) (2019)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://mlsanity.com/paper/2006.07327/) (2020)
- [MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection](https://mlsanity.com/paper/2209.03102/) (2022)
- [FocalFormer3D : Focusing on Hard Instance for 3D Object Detection](https://mlsanity.com/paper/2308.04556/) (2023)
- [CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion](https://mlsanity.com/paper/2209.02540/) (2022)
- [Learnable Online Graph Representations for 3D Multi-Object Tracking](https://mlsanity.com/paper/2104.11747/) (2021)
- [Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking](https://mlsanity.com/paper/2307.16675/) (2023)
- [Poisson multi-Bernoulli mixture filter: direct derivation and implementation](https://mlsanity.com/paper/1703.04264/) (2017)
- [3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention](https://mlsanity.com/paper/2203.10926/) (2022)
- [ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking](https://mlsanity.com/paper/2211.03919/) (2022)
- [Extended Object Tracking: Introduction, Overview and Applications](https://mlsanity.com/paper/1604.00970/) (2016)
- [Scalable Detection and Tracking of Geometric Extended Objects](https://mlsanity.com/paper/2103.11279/) (2021)
- [Neural Enhanced Belief Propagation for Multiobject Tracking](https://mlsanity.com/paper/2212.08340/) (2022)
- [A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors](https://mlsanity.com/paper/1607.07647/) (2016)
- [A Belief Propagation Algorithm for Multipath-Based SLAM](https://mlsanity.com/paper/1801.04463/) (2018)
- [Data Fusion for Multipath-Based SLAM: Combining Information from Multiple Propagation Paths](https://mlsanity.com/paper/2211.09241/) (2022)
- [Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing](https://mlsanity.com/paper/2312.17261/) (2023)
- [Trajectory PHD and CPHD Filters with Unknown Detection Profile](https://mlsanity.com/paper/2111.03863/) (2021)
- [Multisensor Multiobject Tracking with Improved Sampling Efficiency](https://mlsanity.com/paper/2212.14556/) (2022)
- [Bayesian Detection and Tracking of Odontocetes in 3-D from Their Echolocation Clicks](https://mlsanity.com/paper/2210.12318/) (2022)
- [A BP Method for Track-Before-Detect](https://mlsanity.com/paper/2307.00765/) (2023)

## Built on by (most cited first, 3 of 3)

- [Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models](https://mlsanity.com/paper/2506.18124/) (2025)
- [OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering](https://mlsanity.com/paper/2503.12968/) (2025)
- [Distributed Poisson multi-Bernoulli filtering via generalised covariance intersection](https://mlsanity.com/paper/2506.18397/) (2025)

## Abstract (arXiv)

Multiobject tracking (MOT) is an important task in robotics, autonomous driving, and maritime surveillance. Traditional work on MOT is model-based and aims to establish algorithms in the framework of sequential Bayesian estimation. More recent methods are fully data-driven and rely on the training of neural networks. The two approaches have demonstrated advantages in certain scenarios. In particular, in problems where plenty of labeled data for the training of neural networks is available, data-driven MOT tends to have advantages compared to traditional methods. A natural thought is whether a general and efficient framework can integrate the two approaches. This paper advances a recently introduced hybrid model-based and data-driven method called neural-enhanced belief propagation (NEBP). Compared to existing work on NEBP for MOT, it introduces a novel neural architecture that can improve data association and new object initialization, two critical aspects of MOT. The proposed tracking method is leading the nuScenes LiDAR-only tracking challenge at the time of submission of this paper.