# MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark

arXiv: https://arxiv.org/abs/2111.15157 · HTML: https://mlsanity.com/paper/2111.15157/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 26 of 26)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [FastReID: A Pytorch Toolbox for General Instance Re-identification](https://mlsanity.com/paper/2006.02631/) (2020)
- [Features for Multi-Target Multi-Camera Tracking and Re-Identification](https://mlsanity.com/paper/1803.10859/) (2018)
- [MultiGrain: a unified image embedding for classes and instances](https://mlsanity.com/paper/1902.05509/) (2019)
- [Multiview Detection with Feature Perspective Transformation](https://mlsanity.com/paper/2007.07247/) (2020)
- [Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)](https://mlsanity.com/paper/2108.05888/) (2021)
- [Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection](https://mlsanity.com/paper/1704.05775/) (2017)
- [VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild](https://mlsanity.com/paper/2108.02452/) (2021)
- [Deep Multi-camera People Detection](https://mlsanity.com/paper/1702.04593/) (2017)
- [SALSA: A Novel Dataset for Multimodal Group Behavior Analysis](https://mlsanity.com/paper/1506.06882/) (2015)
- [Real-time 3D Deep Multi-Camera Tracking](https://mlsanity.com/paper/2003.11753/) (2020)
- [Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry](https://mlsanity.com/paper/2007.10986/) (2020)
- [Generalizable Multi-Camera 3D Pedestrian Detection](https://mlsanity.com/paper/2104.05813/) (2021)
- [Self-supervised Multi-view Person Association and Its Applications](https://mlsanity.com/paper/1805.08717/) (2018)
- [Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model](https://mlsanity.com/paper/2008.09785/) (2020)
- [Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking](https://mlsanity.com/paper/1911.12037/) (2019)
- [Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets](https://mlsanity.com/paper/1706.06196/) (2017)
- [A unified neural network for object detection, multiple object tracking and vehicle re-identification](https://mlsanity.com/paper/1907.03465/) (2019)

## Built on by (most cited first, 29 of 29)

- [Waymo Open Dataset: Panoramic Video Panoptic Segmentation](https://mlsanity.com/paper/2206.07704/) (2022)
- [CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion](https://mlsanity.com/paper/2212.01247/) (2022)
- [Large Scale Real-World Multi-Person Tracking](https://mlsanity.com/paper/2211.02175/) (2022)
- [In Pursuit of Many: A Review of Modern Multiple Object Tracking Systems](https://mlsanity.com/paper/2209.04796/) (2022)
- [View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV](https://mlsanity.com/paper/2403.10830/) (2024)
- [Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment](https://mlsanity.com/paper/2304.09471/) (2023)
- [MCTR: Multi Camera Tracking Transformer](https://mlsanity.com/paper/2408.13243/) (2024)
- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary](https://mlsanity.com/paper/2212.00266/) (2022)
- [TAO-Amodal: A Benchmark for Tracking Any Object Amodally](https://mlsanity.com/paper/2312.12433/) (2023)
- [A Unified Multi-view Multi-person Tracking Framework](https://mlsanity.com/paper/2302.03820/) (2023)
- [MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark](https://mlsanity.com/paper/2403.20225/) (2024)
- [Anti-Forgetting Adaptation for Unsupervised Person Re-identification](https://mlsanity.com/paper/2411.14695/) (2024)
- [MITracker: Multi-View Integration for Visual Object Tracking](https://mlsanity.com/paper/2502.20111/) (2025)
- [3D Multiple Object Tracking on Autonomous Driving: A Literature Review](https://mlsanity.com/paper/2309.15411/) (2023)
- [GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors](https://mlsanity.com/paper/2402.14136/) (2024)
- [Spatiotemporal Multi-Camera Calibration using Freely Moving People](https://mlsanity.com/paper/2502.12546/) (2025)
- [CrossView Suite: Harnessing Cross-view Spatial Intelligence of MLLMs with Dataset, Model and Benchmark](https://mlsanity.com/paper/2605.18621/) (2026)
- [CARPE-ID: Continuously Adaptable Re-identification for Personalized Robot Assistance](https://mlsanity.com/paper/2310.19413/) (2023)
- [Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras](https://mlsanity.com/paper/2401.14132/) (2024)
- [Training for X-Ray Vision: Amodal Segmentation, Amodal Content Completion, and View-Invariant Object Representation from Multi-Camera Video](https://mlsanity.com/paper/2507.00339/) (2025)
- [To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software](https://mlsanity.com/paper/2508.20892/) (2025)
- [Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey](https://mlsanity.com/paper/2510.09731/) (2025)
- [Enhancing Multi-Camera Gymnast Tracking Through Domain Knowledge Integration](https://mlsanity.com/paper/2511.16532/) (2025)
- [MGSfM: Multi-Camera Geometry Driven Global Structure-from-Motion](https://mlsanity.com/paper/2507.03306/) (2025)
- [Beyond Appearance: A Multi-cue Framework and Large-scale Benchmark for Pedestrian Association and Tracking on Mobile Aerial-Ground Platforms](https://mlsanity.com/paper/2607.23803/) (2026)
- [Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT](https://mlsanity.com/paper/2609.22291/) (2026)
- [MVVBench: Benchmarking 4D Reasoning in Vision-Language Models](https://mlsanity.com/paper/2609.30952/) (2026)
- [Physical AI Smart Spaces: A Large-Scale Benchmark for Multi-Camera 3D Perception in Smart Spaces](https://mlsanity.com/paper/2610.02580/) (2026)

## Abstract (arXiv)

Multi-camera tracking systems are gaining popularity in applications that demand high-quality tracking results, such as frictionless checkout because monocular multi-object tracking (MOT) systems often fail in cluttered and crowded environments due to occlusion. Multiple highly overlapped cameras can significantly alleviate the problem by recovering partial 3D information. However, the cost of creating a high-quality multi-camera tracking dataset with diverse camera settings and backgrounds has limited the dataset scale in this domain. In this paper, we provide a large-scale densely-labeled multi-camera tracking dataset in five different environments with the help of an auto-annotation system. The system uses overlapped and calibrated depth and RGB cameras to build a high-performance 3D tracker that automatically generates the 3D tracking results. The 3D tracking results are projected to each RGB camera view using camera parameters to create 2D tracking results. Then, we manually check and correct the 3D tracking results to ensure the label quality, which is much cheaper than fully manual annotation. We have conducted extensive experiments using two real-time multi-camera trackers and a person re-identification (ReID) model with different settings. This dataset provides a more reliable benchmark of multi-camera, multi-object tracking systems in cluttered and crowded environments. Also, our results demonstrate that adapting the trackers and ReID models on this dataset significantly improves their performance. Our dataset will be publicly released upon the acceptance of this work.