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Video Browsing by Direct
      Manipulation
  Pierre Dragicevic, Gonzato Ramos, Jacobo Bibliowicz,
       Derek Nowrouzezahrai, Ravin Balakrishman,
                       Karan Singh


                   User Interface Design 646
                 Presented by Vashira Ravipanich
                          5171439021
Introduction
•   All video players use
    “seeker bar” to control
    user interaction

•   What if you can directly
    dragging in the movie?
Introduction
• This paper presents a method for browsing
  videos by “directly dragging” their content
• Automatically extracting motion data
• Relative Flow Dragging
Why Direct Manipulation?
• Input ~ Output
• Time V.S. Space
• Both are complementary NOT rival
 Input like finger move = Output like
 mouse movement

 Time = seeker Bar, Space = Direct
 Manipulation
How does it works?
• Videos = sequence of multiple pictures
  (frame)
• Extract object(s) movement   Call “Trajectory
                               Extraction”



• Construct “hint path”
Relative Flow Dragging
• Directness                     Directness => user input lang ==
                                 generated output



• Matching gesture with motion
                                 2D = map
                                 3D = scaling object, rotating object
Type of dragging
•   Curvilinear Dragging

•   Flow Dragging

•   Relative Dragging
Direct Manipulation Video Player - DIMP
Background Stabilization
Position Feedback
Trajectory Extraction
•   Computer Vision Approaches
•   Object Tracking
    -   object on video sequence
    -   motion capture, surveillance
•   Optical Flow
    -   whole picture, calculate pixels
    -   video compression
•   Optical Flow is better for general video player
Curvilinear Dragging Design
Proposed Solutions
• 3D Distance Method
• (x, y, z) where z is arc-length distance from
  the curve origin
Limitations
• Video with back-and-forth movement, i.e a
  couple dancing tango
• DIfficult to visualize path clearly
Evaluation
• User Study
• 6 males, 10 females
• 18 - 44 years old
• Test with 2 videos with given objectives
• Offer both seeker bar and relative flow
  dragging
• Which one user comfortable with the most?
Quantitative Results
Quantitative Results
Previous work on Video Browsing
• Non-Linear Video Browsing
 - Segment of difference importance
 - Estimating motion activity
• Visual Summaries
 - Generate mosaic from key frames
• Content-Based Video Retrieval
Conclusion & Future Work
• New way of browsing videos using direct
   manipulation
 • Appealing to touch-input handheld. iPhone,
   Pocket PC.
 • Interactive Learning Environments.
References
       1. Accot, J. and Zhai, S. (1997). Beyond Fitts' law: mod-   11. Dragicevic, P., Huot, S. and Huot, S. (2002). SpiraC-
els for trajectory-based HCI tasks. CHI. p. 295-302.               lock: a continuous and non-intrusive display for up-
2. Appert, C. and Fekete, J. (2006). OrthoZoom scroller:           coming events. CHI Extended Abstracts. p. 604-605.
1D Multi-Scale Navigation. CHI. P. 21-30.                          12. Goldman, D.B., Curless, B., Salesin, D. and Seitz, S.M.
3. Autodesk Maya. http://www.autodesk.com/                         (2006). Schematic storyboarding for video visualization
4. Baudel, T., Fitzmaurice, G., Buxton, W., Kurtenbach,            and editing. SIGGRAPH. p. 862-871.
G., Tappen, C. and Liepa, P. (2002). Drawing system                13. Guimbretière, F. (2000). FlowMenu: combining com-
using design guides. US Patent # 6,377,240.                        mand, text, and data entry. UIST. p. 213-216.
5. Beauchemin, S.S. and Barron, J.L. (1995). The compu-            14. Hölzl, R. (1996). How does ‘dragging’ affect the learn-
tation of optical flow. ACM Computing Surveys, 27(3).               ing of geometry? International Journal of Computers
p. 433-467.                                                        for Mathematical Learning, 1(2). p. 169-187.
6. Beaudouin-Lafon, M. (2000). Instrumental Interaction:           15. Hutchins, E.L., Hollan, J.D. and Norman, D.A. (1987).
An interaction model for designing post-WIMP user in-              Direct manipulation interfaces. In Human-Computer in-
terfaces. CHI. p. 446-453.                                         teraction: A Multidisciplinary Approach. R. M. Baeck-
7. Beaudouin-Lafon, M. (2001). Novel interaction tech-             er, Ed. Morgan Kaufmann. p. 468-470.
niques for overlapping windows. UIST. p. 153-154.                  16. Irani, M., Anadan, P. and Hsu, H. (1995). Mosaic based
8. Bezerianos, A., Dragicevic, P. and Balakrishnan, R.             representations of video sequences and their applica-
(2006). Mnemonic rendering: an image-based approach                tions. Intl. Conference on Computer Vision. p. 605-611.
for exposing hidden changes in dynamic displays.                   17. Kim, C. and Hwang, J. (2002). Fast and automatic
UIST. p. 159-168.                                                  video object segmentation and tracking for content-
9. Buxton, W. (1986). There's more to interaction than             based applications. IEEE Trans. Circuits and Systems
meets the eye: some issues in manual input. In User                for Video Technology, 12. p. 122-129.
Centered System Design: New Perspectives on Human-                 18. Kimber D., Dunnigan, T., Girgensohn, A., Shipman, F.,
Computer Interaction. Lawrence Erlbaum. p. 19-337.                 Turner, T. and Yang, T. (2007). Trailblazing: Video
10. Cheng,Y. (1995). Mean shift, mode seeking, and clus-           playback control by direct object manipulation. ICME.
tering. IEEE Transactions on Pattern Analysis and Ma-              p. 1015-1018.
chine Intelligence, 17(8). p. 790-799.                             19. Li, F.C., Gupta, A., Sanocki, E., He, L. and Rui, Y.
Thank you

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Video Browsing By Direct Manipulation - Draft 1

  • 1. Video Browsing by Direct Manipulation Pierre Dragicevic, Gonzato Ramos, Jacobo Bibliowicz, Derek Nowrouzezahrai, Ravin Balakrishman, Karan Singh User Interface Design 646 Presented by Vashira Ravipanich 5171439021
  • 2. Introduction • All video players use “seeker bar” to control user interaction • What if you can directly dragging in the movie?
  • 3. Introduction • This paper presents a method for browsing videos by “directly dragging” their content • Automatically extracting motion data • Relative Flow Dragging
  • 4.
  • 5. Why Direct Manipulation? • Input ~ Output • Time V.S. Space • Both are complementary NOT rival Input like finger move = Output like mouse movement Time = seeker Bar, Space = Direct Manipulation
  • 6. How does it works? • Videos = sequence of multiple pictures (frame) • Extract object(s) movement Call “Trajectory Extraction” • Construct “hint path”
  • 7. Relative Flow Dragging • Directness Directness => user input lang == generated output • Matching gesture with motion 2D = map 3D = scaling object, rotating object
  • 8. Type of dragging • Curvilinear Dragging • Flow Dragging • Relative Dragging
  • 12. Trajectory Extraction • Computer Vision Approaches • Object Tracking - object on video sequence - motion capture, surveillance • Optical Flow - whole picture, calculate pixels - video compression • Optical Flow is better for general video player
  • 14. Proposed Solutions • 3D Distance Method • (x, y, z) where z is arc-length distance from the curve origin
  • 15. Limitations • Video with back-and-forth movement, i.e a couple dancing tango • DIfficult to visualize path clearly
  • 16. Evaluation • User Study • 6 males, 10 females • 18 - 44 years old • Test with 2 videos with given objectives • Offer both seeker bar and relative flow dragging • Which one user comfortable with the most?
  • 17.
  • 20. Previous work on Video Browsing • Non-Linear Video Browsing - Segment of difference importance - Estimating motion activity • Visual Summaries - Generate mosaic from key frames • Content-Based Video Retrieval
  • 21. Conclusion & Future Work • New way of browsing videos using direct manipulation • Appealing to touch-input handheld. iPhone, Pocket PC. • Interactive Learning Environments.
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