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Enhancing User Interactions with Video: A Comprehensive Study on Motion Control and Face Recognition Techniques

This study explores how motion control, end call voice control, and face recognition impact user interactions with video contents. It addresses gaps in suitable querying and interface components, selection techniques, and motivation for interaction improvements.

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Enhancing User Interactions with Video: A Comprehensive Study on Motion Control and Face Recognition Techniques

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  1. Enabling User Interactions with Video Contents Khalad Hasan, Yang Wang, Wing Kwongand PourangIrani

  2. 2 Motion Control

  3. 3 End Call Voice Control

  4. 4 Face Recognition

  5. 5 • Gaps • Interaction with video contents • Suitable querying and interface component • Selection technique Motivation

  6. 6 • Gaps • Interaction with video contents • Suitable querying and interface component • Selection technique Motivation

  7. 7 • Computer Vision • Comparison among state-of-art algorithms • Apply best algorithm to extract objects • HCI • Interaction with video contents • Techniques for selection Contribution

  8. 8 Object Detection & Tracking

  9. 9 Datasets: http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml Tracking-Learning-Detection (TLD): Kalal et al.

  10. 10 Struck: Hare et al.

  11. 11 Speed Comparison

  12. 12 Precision

  13. 13 Interactions

  14. 14 Input Device

  15. 15 Kinect Input

  16. Target Static Target Selection

  17. Moving Target Selection

  18. 18 • Left-hand with Basic • Left-hand with Ghost • Left-hand with Crossing • Depth with Basic • Depth with Ghost • Depth with Crossing Selection Techniques

  19. 19 • Left-hand with Basic Selection Techniques

  20. Action Target Ghost (Khalad et al. CHI 2011)

  21. 21 • Left-hand with Ghost Selection Techniques

  22. 22 • Left-hand with Crossing Selection Techniques

  23. 23 • Selection • Left-hand • Depth Selection Techniques

  24. 24 Task Completion Time (ms) 6,000 ― 4,000 ― 2,000 ― 0 ― Left-hand Depth Basic Ghost Crossing Technique Results

  25. 25 Average Number of Attempts 2.0 ― 1.5 ― 1.0 ― 0.5― 0 ― Left-hand Depth Basic Ghost Crossing Technique Results

  26. 26

  27. 27 • TLD is faster & accurate • Both hands for Kinect based interactions • Selection is best achieved with static proxies Take-Home

  28. 28 • Computer Vision • Multiple tracked objects • Online detection & tracking • HCI • Selection Techniques • New form of interactions with Kinect Future work

  29. Thank you

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