mlsanityData to 7 Oct 2026

Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking

arXiv 2203.09948 ↗First posted 17 Mar 2022cs.CV cs.LG eess.SPcited by 10

Not on the map. Why?

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

31 of 48 references.

References · 31

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  9. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  10. 2020FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Trackingcited by 489
  11. 2019Class-balanced Grouping and Sampling for Point Cloud 3D Object Detectioncited by 350
  12. 2019Towards Real-Time Multi-Object Trackingcited by 311
19 more
  1. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  2. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  3. 2019Disentangling Monocular 3D Object Detectioncited by 215
  4. 2020PnPNet: End-to-End Perception and Prediction with Tracking in the Loopcited by 143
  5. 2019Imbalance Problems in Object Detection: A Reviewcited by 118
  6. 2020GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learningcited by 113
  7. 2020Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Drivingcited by 73
  8. 2020Neural Enhanced Belief Propagation on Factor Graphscited by 55
  9. 2021Learnable Online Graph Representations for 3D Multi-Object Trackingcited by 38
  10. 2018Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filteringcited by 32
  11. 2017Poisson multi-Bernoulli mixture filter: direct derivation and implementationcited by 29
  12. 2021TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Trackingcited by 21
  13. 2016Extended Object Tracking: Introduction, Overview and Applicationscited by 16
  14. 2021Scalable Detection and Tracking of Geometric Extended Objectscited by 12
  15. 2016A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensorscited by 8
  16. 2021Neural Enhanced Belief Propagation for Cooperative Localizationcited by 5
  17. 2020A Poisson multi-Bernoulli mixture filter for coexisting point and extended targetscited by 3
  18. 2021Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithmcited by 3
  19. 2021Graph-Based Multiobject Tracking with Embedded Particle Flowcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 4; 2024: 1; 2025: 1.

10 papers.

  1. 2022ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Trackingcited by 21
  2. 20233DMOTFormer: Graph Transformer for Online 3D Multi-Object Trackingcited by 21
  3. 2022GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistlescited by 15
  4. 2022Neural Enhanced Belief Propagation for Multiobject Trackingcited by 11
  5. 2022Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Trackingcited by 9
  6. 2025LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Trackingcited by 2
  7. 2023ShaSTA-Fuse: Camera-LiDAR Sensor Fusion to Model Shape and Spatio-Temporal Affinities for 3D Multi-Object Trackingcited by 1
  8. 2023Pedestrian Environment Model for Automated Drivingcited by 0
  9. 2023Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passingcited by 0
  10. 2024SSP-GNN: Learning to Track via Bilevel Optimizationcited by 0