mlsanityData to 14 Oct 2026

Video Based Fall Detection Using Human Poses

arXiv 2107.14633 ↗First posted 29 Jul 2021cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

Video based fall detection accuracy has been largely improved due to the recent progress on deep convolutional neural networks. However, there still exists some challenges, such as lighting variation, complex background, which degrade the accuracy and generalization ability of these approaches. Meanwhile, large computation cost limits the application of existing fall detection approaches. To alleviate these problems, a video based fall detection approach using human poses is proposed in this paper. First, a lightweight pose estimator extracts 2D poses from video sequences and then 2D poses are lifted to 3D poses. Second, we introduce a robust fall detection network to recognize fall events using estimated 3D poses, which increases respective filed and maintains low computation cost by dilated convolutions. The experimental results show that the proposed fall detection approach achieves a high accuracy of 99.83% on large benchmark action recognition dataset NTU RGB+D and real-time performance of 18 FPS on a non-GPU platform and 63 FPS on a GPU platform.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 19 of its 49 references that are in the map. First posted on arXiv 29 Jul 2021.

Builds on

19 of 49 references.

References · 19

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
  3. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
  4. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
  5. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 686
  6. 2019VIBE: Video Inference for Human Body Pose and Shape Estimationcited by 620
  7. 2018Simple Baselines for Human Pose Estimation and Trackingcited by 502
  8. 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 494
  9. 2017A simple yet effective baseline for 3d human pose estimationcited by 401
  10. 2013DeepPose: Human Pose Estimation via Deep Neural Networkscited by 305
  11. 2014Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimationcited by 129
  12. 2020TransPose: Keypoint Localization via Transformercited by 126
7 more
  1. 2020Epipolar Transformerscited by 117
  2. 20163D Human Pose Estimation = 2D Pose Estimation + Matchingcited by 113
  3. 20183D Human Pose Estimation in the Wild by Adversarial Learningcited by 86
  4. 2019RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimationcited by 85
  5. 2020CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wildcited by 65
  6. 20203D Human Pose Estimation using Spatio-Temporal Networks with Explicit Occlusion Trainingcited by 54
  7. 2019Simple and Lightweight Human Pose Estimationcited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2022Vision-based Human Fall Detection Systems using Deep Learning: A Reviewcited by 7
  2. 2022Elderly Fall Detection Using CCTV Cameras under Partial Occlusion of the Subjects Bodycited by 0
  3. 2024Real-Time Human Fall Detection using a Lightweight Pose Estimation Techniquecited by 0
  4. 2025Human Fall Detection using Transfer Learning-based 3D CNNcited by 0
  5. 2026Stereo Vision-Based Fall Prediction and Detection using Human Pose Estimation on the AMD Kria K26 SOMcited by 0