Video Fields: Fusing Multiple Surveillance Videos into a Dynamic Virtual Environment

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Paper and videos: http://www.videofields.com Another interesting project: http://socialstreetview.com Video Fields system fuses multiple videos, camera-world matrices from a calibration interface, static 3D models, as well as satellite imagery into a novel dynamic virtual environment. Video Fields integrates automatic segmentation of moving entities during the rendering pass and achieves view-dependent rendering in two ways: early pruning and deferred pruning. Video Fields takes advantage of the WebGL and WebVR technology to achieve cross-platform compatibility across smart phones, tablets, desktops, high-resolution tiled curved displays, as well as virtual reality head-mounted displays. See the supplementary video at http://video-fields.com.

Technologie

Video Fields: Fusing Multiple Surveillance Videos into a
Dynamic Virtual Environment
Ruofei Du, Sujal Bista, Amitabh Varshney
The Augmentarium| UMIACS | University of Maryland, College Park
{ruofei, sujal, varshney} @ cs.umd.edu
www.VideoFields.com

image courtesy: university of maryland, college park
Introduction
Surveillance Videos - Monitoring

image courtesy: www.icsc.org
Introduction
Surveillance Videos – Shopping Centers

image courtesy: wikipedia
Introduction
Surveillance Videos - Airports

image courtesy: wikipedia
Introduction
Surveillance Videos – Train stations

image courtesy: university of maryland, college park
Introduction
Surveillance Videos - Campuses

image courtesy: university of maryland, college park
Introduction
Surveillance Videos - Conventional

image courtesy: theimaginativeconservative.org
Introduction
Surveillance Videos – Cognitive Burden

image courtesy: university of maryland, college park
Introduction
Surveillance Videos – Fusing & Interpreting

Related Work
Fusing Multiple Static Photographs

Related Work
Fusing Multiple Dynamic Videos

Related Work
Fusing Multiple Dynamic Videos
RGB

Related Work
Fusing Multiple Dynamic Videos
RGB
RGBD

Related Work
Fusing Multiple Dynamic Videos
SIGGRAPH 2016
Wednesday, 3:30-4:00 PM

Conception, architecting & implementation
Video Fields
A mixed reality system that fuses multiple
surveillance videos into an immersive virtual
environment,

Integrating automatic segmentation of
moving entities
Video Fields Rendering
Real-time fragment shader processing

Two algorithms to fuse multiple videos
Early & deferred pruning
These methods use voxels and meshes respectively
to render moving entities in the video fields

Achieving cross-platform compatibility by
WebGL + Three.js
smartphones, tablets, desktop, high-resolution
large-area wide field of view tiled display walls, as
well as head-mounted displays.

Background Modeling
Motivation
• Provide a background texture for each camera
• Identify moving entities in the rendering stage
• Reduce the network bandwidth requirements

Background Modeling
Gaussian Mixture Models (GMM)

Background Modeling
Advantages [Stauffer and Grimson]
More adaptive with:
• different lighting conditions,
• repetitive motions of scene elements,
• moving entities in slow motion

Video Fields Mapping
Challenges
1. Vertex in the 3D models -> Pixel in the texture space
2. Pixel in the texture space -> Vertex on the ground
• The second is useful for projecting a 2D segmentation
of a moving entity to the 3D world

Video Fields Mapping
Perspective correction

Video Fields Mapping
Depth Map / Hashing Function

Early Pruning for Rendering
Moving Entities
Voxels

Deferred Pruning for
Rendering Moving Entities
Billboards

Visual Comparison
Early Pruning vs. Deferred Pruning

Experimental Results
Early Pruning vs. Deferred Pruning

Future Work
Scale Up - Hundreds of cameras

Future Work
Holoportation with RGB cameras

Acknowledgement
Augmentarium Lab | GVIL | UMIACS

Acknowledgement
NSF | Nvidia | MPower | UMIACS

Video Fields
www.Video-Fields.com
Thank you! Questions or comments?
Ruofei Du and Amitabh Varshney
Augmentarium Lab | GVIL | UMIACS
Web3D 2016

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The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The value of a flexible API Management solution for O...

apidays

💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

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Video Fields: Fusing Multiple Surveillance Videos into a Dynamic Virtual Environment

2. Video Fields: Fusing Multiple Surveillance Videos into a Dynamic Virtual Environment Ruofei Du, Sujal Bista, Amitabh Varshney The Augmentarium| UMIACS | University of Maryland, College Park {ruofei, sujal, varshney} @ cs.umd.edu www.VideoFields.com

3. image courtesy: university of maryland, college park Introduction Surveillance Videos - Monitoring

4. image courtesy: www.icsc.org Introduction Surveillance Videos – Shopping Centers

5. image courtesy: wikipedia Introduction Surveillance Videos - Airports

6. image courtesy: wikipedia Introduction Surveillance Videos – Train stations

7. image courtesy: university of maryland, college park Introduction Surveillance Videos - Campuses

8. image courtesy: university of maryland, college park Introduction Surveillance Videos - Conventional

9. image courtesy: theimaginativeconservative.org Introduction Surveillance Videos – Cognitive Burden

10. image courtesy: university of maryland, college park Introduction Surveillance Videos – Fusing & Interpreting

11. Related Work Fusing Multiple Static Photographs

12. Related Work Fusing Multiple Static Photographs

13. Related Work Fusing Multiple Static Photographs

14. Related Work Fusing Multiple Static Photographs

15. Related Work Fusing Multiple Static Photographs

16.

17. Related Work Fusing Multiple Dynamic Videos

18. Related Work Fusing Multiple Dynamic Videos RGB

19. Related Work Fusing Multiple Dynamic Videos RGB RGBD

20. Related Work Fusing Multiple Dynamic Videos

21. Related Work Fusing Multiple Dynamic Videos

22. Related Work Fusing Multiple Dynamic Videos

23. Related Work Fusing Multiple Dynamic Videos

24. Related Work Fusing Multiple Dynamic Videos

25. Related Work Fusing Multiple Dynamic Videos

26. Related Work Fusing Multiple Dynamic Videos

27. Related Work Fusing Multiple Dynamic Videos

28. Related Work Fusing Multiple Dynamic Videos SIGGRAPH 2016 Wednesday, 3:30-4:00 PM

29. Related Work Fusing Multiple Dynamic Videos

30. Related Work Fusing Multiple Dynamic Videos

31. Related Work Fusing Multiple Dynamic Videos

32. Our Approach?

33. Video Fields

34. Video Fields

35. Introduction Video Field

36.

37. Introduction Video Field

38.

39. Conception, architecting & implementation Video Fields A mixed reality system that fuses multiple surveillance videos into an immersive virtual environment,

40. Integrating automatic segmentation of moving entities Video Fields Rendering Real-time fragment shader processing

41. Two algorithms to fuse multiple videos Early & deferred pruning These methods use voxels and meshes respectively to render moving entities in the video fields

42. Achieving cross-platform compatibility by WebGL + Three.js smartphones, tablets, desktop, high-resolution large-area wide field of view tiled display walls, as well as head-mounted displays.

43. System Overview

44. Architecture Video Fields Flowchart

45. Architecture Video Fields Flowchart

46. Architecture Video Fields Flowchart

47.

48. Architecture Video Fields Flowchart

49. Background Modeling Motivation • Provide a background texture for each camera • Identify moving entities in the rendering stage • Reduce the network bandwidth requirements

50. Background Modeling Gaussian Mixture Models (GMM)

51. Background Modeling Advantages [Stauffer and Grimson] More adaptive with: • different lighting conditions, • repetitive motions of scene elements, • moving entities in slow motion

52. Architecture Video Fields Flowchart

53. Segmentation Moving Entities

54. Background Modeling Gaussian Mixture Models (GMM)

55.

56. Architecture Video Fields Flowchart

57. Visibility Test Plus Opacity Modulation

58.

59. Architecture Video Fields Flowchart

60. Video Fields Mapping Overview

61. Video Fields Mapping Challenges 1. Vertex in the 3D models -> Pixel in the texture space 2. Pixel in the texture space -> Vertex on the ground • The second is useful for projecting a 2D segmentation of a moving entity to the 3D world

62. Video Fields Mapping Projection Mapping

63. Video Fields Mapping Perspective correction

64. Video Fields Mapping Depth Map / Hashing Function

65. Early Pruning for Rendering Moving Entities Voxels

66. Deferred Pruning for Rendering Moving Entities Billboards

67. Visual Comparison Early Pruning vs. Deferred Pruning

68. View-dependent Rendering

69. View-dependent Rendering

70. View-dependent Rendering

71. View-dependent Rendering

72. Experimental Results Early Pruning vs. Deferred Pruning

73.