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A Discriminatively Trained, Multiscale, Deformable Part Model February 24, 2016 Adam Allevato CS 381V University of Texas at Austin Outline Partial matching Non-maximum suppression Train image results Live demo Outline

  1. A Discriminatively Trained, Multiscale, Deformable Part Model February 24, 2016 Adam Allevato CS 381V University of Texas at Austin

  2. Outline ● Partial matching ● Non-maximum suppression ● Train image results ● Live demo

  3. Outline ● Partial matching ● Non-maximum suppression ● Train image results ● Live demo

  4. Partial Matching ● Deformable Part Models allows parts of objects to shift around ● What happens when one of the parts is completely missing? ● What happens when the images are hacked to move parts of them around?

  5. Source Image

  6. Learned HOG Features from INRIA

  7. INRIA Person Dataset Matches

  8. Source Image

  9. Modified Source Image

  10. INRIA Person Dataset Matches

  11. Bad Background = Bad Detection

  12. Blocked Parts ● Take the list of part filter responses in a detection ● One by one, replace their area with black pixels ● Test intersection over union against ground truth

  13. Source Image

  14. Detection

  15. 1 Filter Blocked

  16. 3 Filters Blocked

  17. Degradation (VOC 2010 Detector)

  18. Source Image

  19. 0 Blocked Filters

  20. 1 Blocked Filters

  21. 2 Blocked Filters

  22. 3 Blocked Filters

  23. Degradation (VOC 2007 Detector)

  24. Source Image

  25. 0 Blocked Filters

  26. 1 Blocked Filter

  27. 2 Blocked Filters

  28. 3 Blocked Filters

  29. Degradation (VOC 2007 Detector)

  30. Blocked Filters ● DPM is great against this, especially with canonical views ● Shows robustness to occlusion

  31. Random Window Shifts ● Window is shifted by random amount ● The pixels covered are moved to the gap left behind ● All pixel information is maintained

  32. VOC 2010 Bicycle Detector

  33. Ground Truth

  34. No Shifts

  35. One Shift

  36. Static Parts to the Rescue

  37. One Shift

  38. Two Shifts

  39. Three Shifts

  40. Four Shifts

  41. Does how far we shift affect performance? Averaged across 30 trials!

  42. 10 10-Pixel Shifts Ground truth

  43. Does how many times we shift affect performance?

  44. Does how many times we shift affect performance?

  45. Window Shifts ● DPM is robust to small number of window shifts because some part filters still fire correctly ● More shifts give worse performance ● The shift distance does not have appreciable effect on the detection score loss

  46. Partial Matching ● DPM is robust to object parts moving around ● It can also infer positions of hidden or missing object parts ● Sometimes, IoU can actually increase with occlusion

  47. Outline ● Partial matching ● Non-maximum suppression ● Train image results ● Live demo

  48. Size-Matched Image

  49. Without NMS, N = 10

  50. Without NMS, N = 50 N = 44 N = 3 N = 4

  51. With NMS, N = 3

  52. Overlap = |B i ∩ B j | / |B j | Worse matches B i Better matches B j

  53. NMS Overlap ● 30 closely correlated matches are detected before the second person is detected ● 42 matches before third person is detected ● Repeated detections for similar objects rank similarly ● NMS helps highlight the weaker matches ● Asymmetric overlap metric allows good windows to subsume smaller windows that lie inside

  54. Non-Maximum Supression ● Helps avoid duplicates ● Also helps let the weaker data show itself when a limit is imposed on the total number of matches

  55. Outline ● Partial matching ● Non-maximum suppression ● Train image results ● Live demo

  56. Chicago Elevated Train

  57. VOC 2007 Train Model

  58. VOC 2007 Train Results, N = 1

  59. Without NMS, N=30

  60. Without NMS, N=30 ● Many different modes ● Overall high confusion ● Some lonesome outliers B i B j

  61. Chicago Elevated Train ● Most detected windows contain mostly train ● No single canonical detection window - “lots of trains” ● No window captures the entire train ● No learned DPM for “train” is long enough to capture this shape

  62. Outline ● Partial matching ● Non-maximum suppression ● Train image results ● Live demo

  63. Live Demo ● INRIA person dataset ● VOC 2010 dataset - “chair” ● Can we fool it?

  64. Summary ● Tested matches with parts of objects missing ● Surveyed non-max suppression effects ● Results on train image: technically correct, but still did not capture entire object ● Girshick's library is mature and can be easily integrated into live application

  65. References A Discriminatively Trained, Multiscale, Deformable Part Model. P. Felzenszwalb, D. McAllester, ● D. Ramanan. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008 Original code available on GitHub: https://github.com/rbgirshick/voc-dpm ● My code available on GitHub: https://github.com/Kukanani/voc-dpm ● Images ● http://cdn.collider.com/wp-content/image- base/Movies/P/Princess_Bride/the_princess_bride_movie_image__1_.jpg http://www.planetizen.com/files/images/ChicagoEl.jpg http://www.brinoideas.xyz/wp-content/uploads/2015/11/open-design-living-room-ideas-with- black-drume-pendant-and-blue-sofa-and-unique-glass-coffee-table-and-lovely-black-white-area- rug-and-grey-cream-pouf-also-big-window.jpg http://i.telegraph.co.uk/multimedia/archive/01947/B084FX_1947399c.jpg http://images.glaciermedia.ca/polopoly_fs/1.1346352.1410102588!/fileImage/httpImage/image.j pg_gen/derivatives/landscape_563/10175643-1-jpg.jpg

  67. Live Cam Examples

  68. Input Image

  69. VOC 2010 Person Detector

  70. VOC 2010 Person Detector

  71. Chair Detector

  72. Chair Detector

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