# ArtTrack: Articulated Multi-person Tracking in the Wild

arXiv: https://arxiv.org/abs/1612.01465 · HTML: https://mlsanity.com/paper/1612.01465/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2016.

## Built on by (most cited first, 27 of 27)

- [OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association](https://mlsanity.com/paper/2103.02440/) (2021)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild](https://mlsanity.com/paper/2108.02452/) (2021)
- [End-to-End Trainable Multi-Instance Pose Estimation with Transformers](https://mlsanity.com/paper/2103.12115/) (2021)
- [ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2208.11547/) (2022)
- [The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation](https://mlsanity.com/paper/2110.05132/) (2021)
- [Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming](https://mlsanity.com/paper/2101.01060/) (2021)
- [Dual networks based 3D Multi-Person Pose Estimation from Monocular Video](https://mlsanity.com/paper/2205.00748/) (2022)
- [Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity](https://mlsanity.com/paper/2306.07879/) (2023)
- [CoMotion: Concurrent Multi-person 3D Motion](https://mlsanity.com/paper/2504.12186/) (2025)
- [RAMA: A Rapid Multicut Algorithm on GPU](https://mlsanity.com/paper/2109.01838/) (2021)
- [Ponimator: Unfolding Interactive Pose for Versatile Human-human Interaction Animation](https://mlsanity.com/paper/2510.14976/) (2025)
- [HR-Crime: Human-Related Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2108.00246/) (2021)
- [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://mlsanity.com/paper/2305.13765/) (2023)
- [Estimating Pose from Pressure Data for Smart Beds with Deep Image-based Pose Estimators](https://mlsanity.com/paper/2206.06518/) (2022)
- [Human Modelling and Pose Estimation Overview](https://mlsanity.com/paper/2406.19290/) (2024)
- [Improving Multi-Person Pose Tracking with A Confidence Network](https://mlsanity.com/paper/2310.18920/) (2023)
- [HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation](https://mlsanity.com/paper/2106.04269/) (2021)
- [Direct Dense Pose Estimation](https://mlsanity.com/paper/2204.01263/) (2022)
- [GatedUniPose: A Novel Approach for Pose Estimation Combining UniRepLKNet and Gated Convolution](https://mlsanity.com/paper/2409.07752/) (2024)
- [A Global to Local Double Embedding Method for Multi-person Pose Estimation](https://mlsanity.com/paper/2102.07318/) (2021)
- [Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation](https://mlsanity.com/paper/2106.00566/) (2021)
- [A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition](https://mlsanity.com/paper/2205.11962/) (2022)
- [Real-Time Online Skeleton Extraction and Gesture Recognition on Pepper](https://mlsanity.com/paper/2206.11376/) (2022)
- [ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency Model](https://mlsanity.com/paper/2408.15548/) (2024)
- [LGM-Pose: A Lightweight Global Modeling Network for Real-time Human Pose Estimation](https://mlsanity.com/paper/2506.04561/) (2025)
- [VirtualFencer: Generating Fencing Bouts based on Strategies Extracted from In-the-Wild Videos](https://mlsanity.com/paper/2507.00261/) (2025)

## Abstract (arXiv)

In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos. Our starting point is a model that resembles existing architectures for single-frame pose estimation but is substantially faster. We achieve this in two ways: (1) by simplifying and sparsifying the body-part relationship graph and leveraging recent methods for faster inference, and (2) by offloading a substantial share of computation onto a feed-forward convolutional architecture that is able to detect and associate body joints of the same person even in clutter. We use this model to generate proposals for body joint locations and formulate articulated tracking as spatio-temporal grouping of such proposals. This allows to jointly solve the association problem for all people in the scene by propagating evidence from strong detections through time and enforcing constraints that each proposal can be assigned to one person only. We report results on a public MPII Human Pose benchmark and on a new MPII Video Pose dataset of image sequences with multiple people. We demonstrate that our model achieves state-of-the-art results while using only a fraction of time and is able to leverage temporal information to improve state-of-the-art for crowded scenes.