Estimating Distances Between People using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight

• URL: http://arxiv.org/abs/2303.11520v1
• Date: Tue, 21 Mar 2023 00:50:14 GMT
• Title: Estimating Distances Between People using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight
• Authors: Zhangchi Lu, Mertcan Cokbas, Prakash Ishwar, Jansuz Konrad
• Abstract summary: We propose two approaches for monitoring distances between people indoors. One method leverages a geometric model of the fisheye lens, while the other uses a neural network to predict the 3D-world distance. The algorithms achieve 1-2 ft distance error and over 95% accuracy in detecting social-distance violations.
• Score: 8.337649176647645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unobtrusive monitoring of distances between people indoors is a useful tool in the fight against pandemics. A natural resource to accomplish this are surveillance cameras. Unlike previous distance estimation methods, we use a single, overhead, fisheye camera with wide area coverage and propose two approaches. One method leverages a geometric model of the fisheye lens, whereas the other method uses a neural network to predict the 3D-world distance from people-locations in a fisheye image. To evaluate our algorithms, we collected a first-of-its-kind dataset using single fisheye camera, that comprises a wide range of distances between people (1-58 ft) and will be made publicly available. The algorithms achieve 1-2 ft distance error and over 95% accuracy in detecting social-distance violations.

Related papers

• SDGE: Stereo Guided Depth Estimation for 360$^\circ$ Camera Sets [65.64958606221069]
  Multi-camera systems are often used in autonomous driving to achieve a 360$circ$ perception. These 360$circ$ camera sets often have limited or low-quality overlap regions, making multi-view stereo methods infeasible for the entire image. We propose the Stereo Guided Depth Estimation (SGDE) method, which enhances depth estimation of the full image by explicitly utilizing multi-view stereo results on the overlap.
  arXiv  Detail & Related papers  (2024-02-19T02:41:37Z)
• Large-Scale Person Detection and Localization using Overhead Fisheye Cameras [40.004888590123954]
  We present the first large-scale overhead fisheye dataset for person detection and localization. We build a fisheye person detection network, which exploits the fisheye distortions by a rotation-equivariant training strategy. Our whole fisheye positioning solution is able to locate all persons in FOV with an accuracy of 0.5 m, within 0.1 s.
  arXiv  Detail & Related papers  (2023-07-17T05:36:01Z)
• Scene-Aware 3D Multi-Human Motion Capture from a Single Camera [83.06768487435818]
  We consider the problem of estimating the 3D position of multiple humans in a scene as well as their body shape and articulation from a single RGB video recorded with a static camera. We leverage recent advances in computer vision using large-scale pre-trained models for a variety of modalities, including 2D body joints, joint angles, normalized disparity maps, and human segmentation masks. In particular, we estimate the scene depth and unique person scale from normalized disparity predictions using the 2D body joints and joint angles.
  arXiv  Detail & Related papers  (2023-01-12T18:01:28Z)
• SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation [101.55622133406446]
  We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets.
  arXiv  Detail & Related papers  (2022-04-07T17:58:47Z)
• AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation [51.17610485589701]
  We present a novel markerless 3D human motion capture (MoCap) system for unstructured, outdoor environments. AirPose estimates human pose and shape using images captured by multiple uncalibrated flying cameras. AirPose itself calibrates the cameras relative to the person instead of relying on any pre-calibration.
  arXiv  Detail & Related papers  (2022-01-20T09:46:20Z)
• SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras [30.480562747903186]
  A 360deg perception of scene geometry is essential for automated driving, notably for parking and urban driving scenarios. We present novel camera-geometry adaptive multi-scale convolutions which utilize the camera parameters as a conditional input. We evaluate our approach on the Fisheye WoodScape surround-view dataset, significantly improving over previous approaches.
  arXiv  Detail & Related papers  (2021-04-09T15:20:20Z)
• Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm [78.88882860340797]
  COVID-19 virus has caused a global pandemic since March 2020. Maintaining a minimum of one meter distance from other people is strongly suggested to reduce the risk of infection. There is no suitable test benchmark for such algorithms.
  arXiv  Detail & Related papers  (2021-03-11T16:15:20Z)
• Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot [15.815583594196488]
  We present a real time approach for estimating the distances to multiple objects in a scene using only a single-shot image. We let the predictors catch the distance prior using anchor distance and train the network based on the distance. The proposed method achieves about 30 FPS speed, and shows the lowest RMSE compared to the existing methods.
  arXiv  Detail & Related papers  (2021-01-25T20:33:05Z)
• Perceiving Humans: from Monocular 3D Localization to Social Distancing [93.03056743850141]
  We present a new cost-effective vision-based method that perceives humans' locations in 3D and their body orientation from a single image. We show that it is possible to rethink the concept of "social distancing" as a form of social interaction in contrast to a simple location-based rule.
  arXiv  Detail & Related papers  (2020-09-01T10:12:30Z)
• SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving [37.50089104051591]
  State-of-the-art self-supervised learning approaches for monocular depth estimation usually suffer from scale ambiguity. This paper introduces a novel multi-task learning strategy to improve self-supervised monocular distance estimation on fisheye and pinhole camera images.
  arXiv  Detail & Related papers  (2020-08-10T10:52:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.