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

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

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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 - PowerPoint PPT Presentation

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Topic regards:◆ Browsing of Search Results ◆ Video Retrieval using Spatio-Temporal◆ Object Tracking ◆ Face tracking

Yuan-Hao Lai

Visual Islands: IntuitiveBrowsing of Visual Search ResultsEric Zavesky, Shih-Fu Chang, Cheng-Chih Yang Columbia University International conference on Content-based Image and Video Retrieval (2008)

[Conventional and new approaches]

[Visual Islands]

[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

[Axis Layout]

[Visual Island / Non-Linear Navigation]

[Speed & Accuracy Evaluation]

Object Tracking: A Survey

Alper Yilmaz, Omar Javed, Mubarak ShahOhio State University, ObjectVideo, Inc., University of Central FloridaJournal ACM Computing Surveys (CSUR) (2006)

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

[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

[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

[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

Video Retrieval using Spatio-Temporal DescriptorsDaniel DeMenthon, David DoermannUniversity of Maryland MULTIMEDIA Proceedings of the eleventh ACM international conference(2003)

[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

[Video Strands]

[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

[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

Online learning of robust object detectors during unstable trackingZdenek Kalal, Jiri Matas, Krystian MikolajczykUniversity of Surrey, Czech Technical UniversityComputer Vision Workshops(2009)

[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

[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

[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

[Online model]

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

[ABOLUTE, CHANGE, LOOP strategy]

[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

Thank You.