Transcript Object Tracking

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
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 Shah
Ohio State University, ObjectVideo, Inc., University of Central Florida
Journal 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 Descriptors
Daniel DeMenthon, David Doermann
University 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 tracking
Zdenek Kalal, Jiri Matas, Krystian Mikolajczyk
University of Surrey, Czech Technical University
Computer 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-LearningDetection applied to faces
Zdenek Kalal, Krystian Mikolajczyk, Jiri Matas
University of Surrey, Czech Technical University
Image 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.