Skeltrack - Open Source Skeleton Tracking

Presentation of Skeltrack -- the only Open Source library for skeleton tracking.

Transcript

1. Skeltrack - Open Source Skeleton Tracking Joaquim Rocha, Igalia LinuxTag 2012 - Wunderbare Berlin
2. Guten Tag! ✩ I am a developer at Igalia ✩ I like doing innovative stuff like OCRFeeder and SeriesFinale ✩ and today I am presenting my latest project: Skeltrack
3. The Kinect
4. Microsoft's Kinect was the first camera with a price affordable to the public
5. The USB connection is open and thus hackable
6. This originated Open Source projects like the libfreenect, a library to control the Kinect device and get its information
7. We created a GLib wrapper for libfreenect called GFreenect
8. GFreenect offers asynchronous functions (and some synchronous as well) and makes it easy to use with other GNOME technologies
9. GObject Introspection = free bindings (Python, Javascript, Vala)
10. Kinect has a structured light camera which gives depth information
11. But that's raw information... values from 0-2048
12. libfreenect/GFreenect can give those values in mm
14. Still...
15. It does NOT tell you there is a person in the picture
16. Or a cow
17. Or an ampelmann
18. Let alone a skeleton and where its joints are
19. For this you need a skeleton tracking solution
20. Three proprietary/closed solutions exist:
21. Microsoft Kinect SDK: non-commercial only
22. OpenNI: commercial compatible
23. Kinect for Windows: commercial use allowed but incompatible with the XBox's Kinect
25. Conclusion: There were no Free solutions to perform skeleton tracking... :(
26. So Igalia built one!
27. Enter Skeltrack
28. What we wanted: ✩ A shared library, no fancy SDK ✩ Device independent ✩ No pattern matching, no databases ✩ Easy to use (everybody wants that!)
29. Not as easy as it sounds!
30. After some investigation we found Andreas Baak's paper "A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera"
31. However this paper uses a database of poses to get what the user is doing
32. So we based only part of our work on it
33. How does it work?
34. First we need to find the extremas
35. Make a graph whose nodes are the depth pixels
36. Connect two nodes if the distance is less than a certain value
37. Connect the different graph's components by using connected-component labeling
38. Choose a starting point and calculate Dijkstra to each point of the graph; choose the furthest point. There you got your extrema!
39. Then create an edge between the starting point and the current extrema point with 0 cost and repeat the same process now using the current extrema as a starting point.
40. This comes from Baak's paper and the difference starts here: choosing the starting point
41. Baak chooses a centroid as the starting point We choose the bottom-most point starting from the centroid (this showed better results for the upper body extremas)
42. So we got ourselves some extremas! What to do with them?
43. What extrema is a hand, a head, a shoulder?
44. For that we use educated guesses...
45. We calculate 3 extremas
46. Then we check each of them hoping they are the head
47. How?
48. For each extrema we look for the points in places where the shoulders should be, checking their distances between the extrema and between each other.
49. If they obey those rules then we assume they are the head'n'shoulders (tm)
50. With the remaining 2 extremas, we will try to see if they are elbows or hands
51. How to do it?
52. Calculate Dijkstra from the shoulders to each extrema
53. The closest extrema to any of the shoulders is either a hand of an elbow of that shoulder
54. How to check if it's a hand or an elbow?
55. If the distance between the extrema and the shoulder is less than a predefined value, then it is an elbow. Otherwise it is a hand.
56. If it is a hand, we find the elbow by choosing the first point (in the path we created with Dijkstra before) whose distance exceeds the elbow distance mentioned before
58. There is still some things missing...
59. Future work
60. Hands from elbows: If one of the extremas is an elbow, we need to infer where the hand is
61. Smoothing: Smooth the jittering of the joints
62. Robustness: Use restrictions to ignore objects that are not the user
63. Multi-user: Track more than one person at a time
64. And of course, get the rest of the joints: hips, knees, etc.
65. How to use it?
66. Asynchronous API
67. SkeltrackSkeleton *skeleton = SKELTRACK_SKELETON (skeltrack_skeleton_new ()); skeltrack_skeleton_track_joints (skeleton, depth_buffer, buffer_width, buffer_height, NULL, on_track_joints, NULL);
69. Synchronous API
70. SkeltrackJointList list; list = skeltrack_skeleton_track_joints_sync (skeleton, depth_buffer, buffer_width, buffer_height, NULL, NULL);
71. Skeleton Joint: ID: HEAD, LEFT_ELBOW, RIGHT_HAND, ... x: X coordinate in real world (in mm) y: Y coordinate in real world (in mm) screen_x: X coordinate in the screen (in pixels) screen_y: Y coordinate in the screen (in pixels)
72. Code/Bugs: https://github.com/joaquimrocha/Skeltrack
73. Nifty Tools for Development: GFreenect: https://github.com/elima/GFreenect GFreenect Utils: https://github.com/joaquimrocha/gfreenect-utils
74. GFreenect Python Example
75. Tool: record-depth-file
76. Tool: depth-file-viewer
77. Questions?
78. Creative Commons pictures from flickr: Kinect: Auxo.co.kr Ampelmann: echiner1 Kid Playing: Rob Welsh Skeleton: Dark Botxy