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Topic regards: ◆ Browsing of Search Results ◆ Video Retrieval using Spatio-Temporal

Topic regards: ◆ Browsing of Search Results ◆ Video Retrieval using Spatio-Temporal ◆ Object Tracking ◆ Face tracking. Yuan-Hao Lai. Visual Islands: Intuitive Browsing of Visual Search Results. Eric Zavesky, Shih-Fu Chang, Cheng-Chih Yang Columbia University

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Topic regards: ◆ Browsing of Search Results ◆ Video Retrieval using Spatio-Temporal

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  1. Topic regards: ◆Browsing of Search Results ◆Video Retrieval using Spatio-Temporal ◆Object Tracking ◆Face tracking Yuan-Hao Lai

  2. Visual Islands: Intuitive Browsing of Visual Search Results Eric Zavesky, Shih-Fu Chang, Cheng-Chih Yang Columbia University International conference on Content-based Image and Video Retrieval (2008)

  3. [Conventional and new approaches]

  4. [Visual Islands]

  5. [Significant Advantages] • Make local choices of features that is currently displayed, not all results. • Faster because far fewer sample • Original rank was determined by a direct query from the user instead of a ranking based on similarity

  6. [Axis Layout]

  7. [Visual Island / Non-Linear Navigation]

  8. [Speed & Accuracy Evaluation]

  9. Object Tracking: A Survey Alper Yilmaz, Omar Javed, Mubarak Shah Ohio State University, ObjectVideo, Inc., University of Central Florida Journal ACM Computing Surveys (CSUR) (2006)

  10. [Tracking problems] • Abrupt object motion • Changing appearance (object/scene) • Nonrigid object structures • Obj-to-obj, obj-to-scene occlusions • Camera motion

  11. [Key steps in video analysis] • Detection of interesting moving objects • Tracking of object from frame to frame • Analysis of object tracks to recognize their behavior

  12. [Feature Selection For Tracking] • Color • Spectral power distribution of the illuminant • Surface reflectance properties of the object • Edges • Generate strong changes in image intensities • Less sensitive to illumination changes

  13. [Feature Selection For Tracking] • Optical Flow • Translation of each pixel in a region • Feature in motion-based segmentation and tracking applications • Texture • Quantifies such as smoothness and regularity • Requires a processing step to generate descriptors

  14. Video Retrieval using Spatio-Temporal Descriptors Daniel DeMenthon, David Doermann University of Maryland MULTIMEDIA Proceedings of the eleventh ACM international conference(2003)

  15. [Motivation] • Is an advertising spot been cut by a warning about weather conditions? • Want to jump back to most exciting moments (slow-motion replays) in a football game. • Verify a suspicion of unauthorized access by an outsider of the door system

  16. [Video Strands]

  17. [Indexing and retrieval] • Space-time segmentation • Transform the dynamic content of video clips into simple purely geometric patterns • Dynamic interaction of color regions in videos for the use of pattern recognition techniques • Combining k-nearest neighbors search and voting on the retrieved labels

  18. [Indexing and retrieval] • High level of resilience against video clip variability caused by editing and overlays. • Provide good discriminative power in recognition of actions occurring at fixed places in the field of view of fixed surveillance cameras

  19. Online learning of robust object detectors during unstable tracking Zdenek Kalal, Jiri Matas, Krystian Mikolajczyk University of Surrey, Czech Technical University Computer Vision Workshops(2009)

  20. [Investigates the problem of ] • Visual tracking of unknown objects in unconstrained environments • Cope with frame-cuts, fast camera movements • Partial/total object occlusions/dissapearances • ”Long-term” – possibly infinite length • ”Online” – Need no information from the future

  21. [Standard tracking approaches] • Static models • Object appearance change is limited and known. Unexpected changes of the object appearance can not be tracked • Adaptive method • Update the object model during tracking. But incorrect update brings error

  22. [Adaptive tracker] • Trajectory is observed by two processes (growing and pruning event) • Both events make errors, the stability of the system is achieved by their cancelation • The learnt detector enables re-initialization of the tracker whenever previously observed appearance reoccurs

  23. [Online model]

  24. Face-TLD: Tracking-Learning- Detection applied to faces Zdenek Kalal, Krystian Mikolajczyk, Jiri Matas University of Surrey, Czech Technical University Image Processing (ICIP) 17th IEEE International Conference (2010)

  25. [ABOLUTE, CHANGE, LOOP strategy]

  26. [Contributions] • Addresses the long term tracking problem • Learning method based on two events that boot straps the object model from a single click • Efficient detector structure enabling real-time learning/classification

  27. Thank You.

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