mlsanityData to 14 Oct 2026

Integrated In-vehicle Monitoring System Using 3D Human Pose Estimation and Seat Belt Segmentation

arXiv 2204.07946 ↗First posted 17 Apr 2022cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Recently, along with interest in autonomous vehicles, the importance of monitoring systems for both drivers and passengers inside vehicles has been increasing. This paper proposes a novel in-vehicle monitoring system the combines 3D pose estimation, seat-belt segmentation, and seat-belt status classification networks. Our system outputs various information necessary for monitoring by accurately considering the data characteristics of the in-vehicle environment. Specifically, the proposed 3D pose estimation directly estimates the absolute coordinates of keypoints for a driver and passengers, and the proposed seat-belt segmentation is implemented by applying a structure based on the feature pyramid. In addition, we propose a classification task to distinguish between normal and abnormal states of wearing a seat belt using results that combine 3D pose estimation with seat-belt segmentation. These tasks can be learned simultaneously and operate in real-time. Our method was evaluated on a private dataset we newly created and annotated. The experimental results show that our method has significantly high performance that can be applied directly to real in-vehicle monitoring systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 32 references that are in the map. First posted on arXiv 17 Apr 2022.

Builds on

23 of 32 references.

References · 23

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  5. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
  6. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 724
  7. 2018Simple Baselines for Human Pose Estimation and Trackingcited by 502
  8. 2017A simple yet effective baseline for 3d human pose estimationcited by 401
  9. 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 314
  10. 2017Integral Human Pose Regressioncited by 256
  11. 2016Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Posecited by 221
  12. 2017Single-Shot Multi-Person 3D Pose Estimation From Monocular RGBcited by 195
11 more
  1. 2019Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Imagecited by 132
  2. 2017Compositional Human Pose Regressioncited by 124
  3. 2021PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimationcited by 89
  4. 2019XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Cameracited by 68
  5. 2020HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimationcited by 44
  6. 2020Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimationcited by 36
  7. 2020Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Trainingcited by 27
  8. 2020HDNet: Human Depth Estimation for Multi-Person Camera-Space Localizationcited by 20
  9. 2018Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fieldscited by 7
  10. 2020LiftFormer: 3D Human Pose Estimation using attention modelscited by 7
  11. 2019NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networkscited by 3

Built on by

Not cited yet.