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Szloca: towards a framework for full 3D tracking through a single camera in context of interactive arts

arXiv 2206.12958 ↗First posted 26 Jun 2022cs.CV cs.HC cs.MMcited by 0

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Abstract (arXiv)

Realtime virtual data of objects and human presence in a large area holds a valuable key in enabling many experiences and applications in various industries and with exponential rise in the technological development of artificial intelligence, computer vision has expanded the possibilities of tracking and classifying things through just video inputs, which is also surpassing the limitations of most popular and common hardware setups known traditionally to detect human pose and position, such as low field of view and limited tracking capacity. The benefits of using computer vision in application development is large as it augments traditional input sources (like video streams) and can be integrated in many environments and platforms. In the context of new media interactive arts, based on physical movements and expanding over large areas or gallaries, this research presents a novel way and a framework towards obtaining data and virtual representation of objects/people - such as three-dimensional positions, skeltons/pose and masks from a single rgb camera. Looking at the state of art through some recent developments and building on prior research in the field of computer vision, the paper also proposes an original method to obtain three dimensional position data from monocular images, the model does not rely on complex training of computer vision systems but combines prior computer vision research and adds a capacity to represent z depth, ieto represent a world position in 3 axis from a 2d input source.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 62 references that are in the map. First posted on arXiv 26 Jun 2022.

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24 of 62 references.

References · 24

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  2. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
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  6. 2016Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Imagecited by 695
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  9. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  10. 20183D human pose estimation in video with temporal convolutions and semi-supervised trainingcited by 403
  11. 2017A simple yet effective baseline for 3d human pose estimationcited by 401
  12. 2017Integral Human Pose Regressioncited by 256
12 more
  1. 2018Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodiescited by 239
  2. 2020Consistent Video Depth Estimationcited by 220
  3. 2019Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Imagecited by 132
  4. 2019Fast and Robust Multi-Person 3D Pose Estimation from Multiple Viewscited by 83
  5. 2017Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Imagecited by 73
  6. 2019XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Cameracited by 68
  7. 2017Detect-and-Track: Efficient Pose Estimation in Videoscited by 51
  8. 2019Pano Popups: Indoor 3D Reconstruction with a Plane-Aware Networkcited by 25
  9. 2019Video Depth Estimation by Fusing Flow-to-Depth Proposalscited by 23
  10. 2017Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goalscited by 21
  11. 2019Multi-Person 3D Human Pose Estimation from Monocular Imagescited by 19
  12. 2019Consensus-based Optimization for 3D Human Pose Estimation in Camera Coordinatescited by 9

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