# Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking

arXiv: https://arxiv.org/abs/2203.09948 · HTML: https://mlsanity.com/paper/2203.09948/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 48 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection](https://mlsanity.com/paper/1908.09492/) (2019)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021)
- [Disentangling Monocular 3D Object Detection](https://mlsanity.com/paper/1905.12365/) (2019)
- [PnPNet: End-to-End Perception and Prediction with Tracking in the Loop](https://mlsanity.com/paper/2005.14711/) (2020)
- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://mlsanity.com/paper/2006.07327/) (2020)
- [Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2012.13755/) (2020)
- [Neural Enhanced Belief Propagation on Factor Graphs](https://mlsanity.com/paper/2003.01998/) (2020)
- [Learnable Online Graph Representations for 3D Multi-Object Tracking](https://mlsanity.com/paper/2104.11747/) (2021)
- [Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering](https://mlsanity.com/paper/1802.09975/) (2018)
- [Poisson multi-Bernoulli mixture filter: direct derivation and implementation](https://mlsanity.com/paper/1703.04264/) (2017)
- [TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking](https://mlsanity.com/paper/2101.04206/) (2021)
- [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)
- [A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors](https://mlsanity.com/paper/1607.07647/) (2016)
- [Neural Enhanced Belief Propagation for Cooperative Localization](https://mlsanity.com/paper/2105.12903/) (2021)
- [A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets](https://mlsanity.com/paper/2011.04464/) (2020)
- [Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm](https://mlsanity.com/paper/2108.02573/) (2021)

## Built on by (most cited first, 10 of 10)

- [ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking](https://mlsanity.com/paper/2211.03919/) (2022)
- [3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking](https://mlsanity.com/paper/2308.06635/) (2023)
- [GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles](https://mlsanity.com/paper/2206.10255/) (2022)
- [Neural Enhanced Belief Propagation for Multiobject Tracking](https://mlsanity.com/paper/2212.08340/) (2022)
- [Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking](https://mlsanity.com/paper/2208.10056/) (2022)
- [LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking](https://mlsanity.com/paper/2505.12753/) (2025)
- [ShaSTA-Fuse: Camera-LiDAR Sensor Fusion to Model Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking](https://mlsanity.com/paper/2310.02532/) (2023)
- [Pedestrian Environment Model for Automated Driving](https://mlsanity.com/paper/2308.09080/) (2023)
- [Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing](https://mlsanity.com/paper/2310.12407/) (2023)
- [SSP-GNN: Learning to Track via Bilevel Optimization](https://mlsanity.com/paper/2407.04308/) (2024)

## Abstract (arXiv)

Situation-aware technologies enabled by multiobject tracking (MOT) methods will create new services and applications in fields such as autonomous navigation and applied ocean sciences. Belief propagation (BP) is a state-of-the-art method for Bayesian MOT but fully relies on a statistical model and preprocessed sensor measurements. In this paper, we establish a hybrid method for model-based and data-driven MOT. The proposed neural enhanced belief propagation (NEBP) approach complements BP by information learned from raw sensor data with the goal to improve data association and to reject false alarm measurements. We evaluate the performance of our NEBP approach for MOT on the nuScenes autonomous driving dataset and demonstrate that it can outperform state-of-the-art reference methods.