# Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2204.09151 · HTML: https://mlsanity.com/paper/2204.09151/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 48 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [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)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [End-to-End Video Instance Segmentation with Transformers](https://mlsanity.com/paper/2011.14503/) (2020)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection](https://mlsanity.com/paper/1908.09492/) (2019)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [Disentangling Monocular 3D Object Detection](https://mlsanity.com/paper/1905.12365/) (2019)
- [Scaling Neural Machine Translation](https://mlsanity.com/paper/1806.00187/) (2018)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://mlsanity.com/paper/2006.07327/) (2020)
- [Monocular Quasi-Dense 3D Object Tracking](https://mlsanity.com/paper/2103.07351/) (2021)
- [Joint Monocular 3D Vehicle Detection and Tracking](https://mlsanity.com/paper/1811.10742/) (2018)
- [Online Multi-Object Tracking with Dual Matching Attention Networks](https://mlsanity.com/paper/1902.00749/) (2019)
- [Probabilistic 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2001.05673/) (2020)

## Built on by (most cited first, 20 of 20)

- [Type-to-Track: Retrieve Any Object via Prompt-based Tracking](https://mlsanity.com/paper/2305.13495/) (2023)
- [HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding](https://mlsanity.com/paper/2312.03050/) (2023)
- [QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering](https://mlsanity.com/paper/2405.19722/) (2024)
- [VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer](https://mlsanity.com/paper/2311.06623/) (2023)
- [Depth Perspective-aware Multiple Object Tracking](https://mlsanity.com/paper/2207.04551/) (2022)
- [Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach](https://mlsanity.com/paper/2211.09663/) (2022)
- [Z-GMOT: Zero-shot Generic Multiple Object Tracking](https://mlsanity.com/paper/2305.17648/) (2023)
- [BRACTIVE: A Brain Activation Approach to Human Visual Brain Learning](https://mlsanity.com/paper/2405.18808/) (2024)
- [COBRA: A Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2411.17475/) (2024)
- [Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI](https://mlsanity.com/paper/2312.00236/) (2023)
- [UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation](https://mlsanity.com/paper/2306.09613/) (2023)
- [CarcassFormer: An End-to-end Transformer-based Framework for Simultaneous Localization, Segmentation and Classification of Poultry Carcass Defect](https://mlsanity.com/paper/2404.11429/) (2024)
- [Inverse Neural Rendering for Explainable Multi-Object Tracking](https://mlsanity.com/paper/2404.12359/) (2024)
- [DINTR: Tracking via Diffusion-based Interpolation](https://mlsanity.com/paper/2410.10053/) (2024)
- [TrackMe:A Simple and Effective Multiple Object Tracking Annotation Tool](https://mlsanity.com/paper/2410.15518/) (2024)
- [MGSfM: Multi-Camera Geometry Driven Global Structure-from-Motion](https://mlsanity.com/paper/2507.03306/) (2025)
- [BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2508.18187/) (2025)
- [Stable Multi-Drone GNSS Tracking System for Marine Robots](https://mlsanity.com/paper/2511.18694/) (2025)
- [TriBand-BEV: Real-Time LiDAR-Only 3D Pedestrian Detection via Height-Aware BEV and High-Resolution Feature Fusion](https://mlsanity.com/paper/2605.12220/) (2026)
- [UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2605.19290/) (2026)

## Abstract (arXiv)

The development of autonomous vehicles provides an opportunity to have a complete set of camera sensors capturing the environment around the car. Thus, it is important for object detection and tracking to address new challenges, such as achieving consistent results across views of cameras. To address these challenges, this work presents a new Global Association Graph Model with Link Prediction approach to predict existing tracklets location and link detections with tracklets via cross-attention motion modeling and appearance re-identification. This approach aims at solving issues caused by inconsistent 3D object detection. Moreover, our model exploits to improve the detection accuracy of a standard 3D object detector in the nuScenes detection challenge. The experimental results on the nuScenes dataset demonstrate the benefits of the proposed method to produce SOTA performance on the existing vision-based tracking dataset.