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Real-Time Hand Detection and Tracking Challenges: Insights from Research

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morwenna-awena
38 diapositives · 474 Views · Nov 05, 2014

Discover the challenges and solutions in real-time hand detection and tracking technology, including issues with self-occlusions, unreliable point features, and background clutter. Explore 3D tracking techniques and robust tracking strategies to handle complex backgrounds and varying illumination for improved object localization.

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Real-Time Hand Detection and Tracking Challenges: Insights from Research
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  1. Looking at people and Image-based Localisation Roberto Cipolla Department of Engineering Research team http://www.eng.cam.ac.uk/~cipolla/people.html

  2. 1. Real-time hand detection and tracking

  3. Why is it hard? • Highly articulated object, 27 model parameters • Shape variation and self-occlusions • Unreliable point features • Ambiguities in single view lead to multi-modal distributions (local minima)

  4. Why is it hard? • Background clutter • Potentially fast motion • Lighting changes • Partial / full occlusion

  5. A Solved Problem? 3D tracking, 6/7 DOF • Model: 3D quadrics • Cost Function: Edges or colour-edges • Tracking: Unscented Kalman filtering • Single or dual view • Single hypothesis filter, no recovery strategy

  6. A Robust Tracker • Should work in scenes with complex background and varying illumination • Important: Cost function design • Optimization strategy • Should handle multi-modality • Examples: Particle filters, multi-hypotheses filters • Should have a recovery strategy when track is lost • Trigger search algorithm

  7. 3D Pose Recovery 3D hand model constructed from cones and ellipsoids Contour projection, handling self-occlusions 27 motion parameters

  8. Hierarchy of classifiers

  9. Likelihood : Edges 3D Model Input Image Edge Detection Projected Contours Robust Edge Matching

  10. Chamfer Matching Input image Canny edges Distance transform Projected Contours

  11. Likelihood : Colour 3D Model Input Image Projected Silhouette Skin Colour Model Template Matching

  12. Tree-based bayesian filtering

  13. Matching Multiple Templates • Use tree structure to efficiently match many templates (>50,000) • Arrange templates in tree based on their similarity • Traverse tree using breadth-first search, several ‘active’ leaves possible Search Tree Grid-based partitioning ofparameter space

  14. Bayesian-Tree State space partitioning Estimation of posterior pdf • The search-tree is brought into a Bayesian framework by adding the prior knowledge from previous frame. • The Bayesian-Tree can be thought as approximating the posterior probability at different resolutions.

  15. Experiments Global Motion • 3D motions limited to hemisphere • Dynamics: First-order Gaussian process • 3 level tree with 16,000 templates at leaf level • 5 scales, divisions of 15 degrees in 3D rotation and divisions of 10 degrees in image plane rotation • Translation search at 20, 5, 2-pixel resolution

  16. Tracking Results

  17. Tracking Results

  18. Experiments Finger Articulation • Opening and closing of thumb and fingers approximated by 2 parameters • Global motion restricted to smaller range, but still with 6 DOF • 35,000 templates at the leaf level

  19. Opening and closing

  20. Hand detection system

  21. Ongoing work • Large number of templates required Examples shown here show only constrained motion Number of templates required for fully articulated motion? • Tracking rates at 5 fps to 0.2 fps For 400 - 35,000 templates(on a 2.4 GHz Pentium IV) • Error introduced by geometric model No palm deformation, no skin deformation, no arm model

  22. Detecting people

  23. 2. Building 3D models of cities

  24. Trumpington Street Data

  25. Camera pose determination

  26. 3D reconstruction

  27. Reconstruction texture mapped

  28. 3. Where am I?

  29. Image-based localisation ... ...

  30. Image-based localisation

  31. Image-based localisation … …

  32. Image-based localisation

  33. Image-based localisation

  34. Image-based localisation

  35. Image-based localisation

  36. Image-based localisation

  37. Image-based localisation

  38. Summary and deliverables • Realtime hand detection in clutter • 3D models from uncalibrated images • Image-based localisation for augmented reality