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behind obstructions. Additionally the complexity is increased if the video tracker (also named TV tracker or target tracker) is not mounted on rigid foundation (on-shore) but on a moving ship (off-shore), where typically an inertial measurement system is used to pre-stabilize the video tracker to reduce the required dynamics and bandwidth of the camera system. The computational complexity for these algorithms is usually much higher. The following are some common filtering algorithms:
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195:: an optimal recursive Bayesian filter for linear functions subjected to Gaussian noise. It is an algorithm that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone.
79:. Another situation that increases the complexity of the problem is when the tracked object changes orientation over time. For these situations video tracking systems usually employ a motion model which describes how the image of the target might change for different possible motions of the object.
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is mostly a top-down process, which involves incorporating prior information about the scene or object, dealing with object dynamics, and evaluation of different hypotheses. These methods allow the tracking of complex objects along with more complex object interaction like tracking objects moving
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is mostly a bottom-up process. These methods give a variety of tools for identifying the moving object. Locating and tracking the target object successfully is dependent on the algorithm. For example, using blob tracking is useful for identifying human movement because a person's profile changes
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and outputs the movement of targets between the frames. There are a variety of algorithms, each having strengths and weaknesses. Considering the intended use is important when choosing which algorithm to use. There are two major components of a visual tracking system: target representation and
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180:). Contour tracking methods iteratively evolve an initial contour initialized from the previous frame to its new position in the current frame. This approach to contour tracking directly evolves the contour by minimizing the contour energy using gradient descent.
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The objective of video tracking is to associate target objects in consecutive video frames. The association can be especially difficult when the objects are moving fast relative to the
263:"Three-Dimensional Tissue Deformation Recovery and Tracking: Introducing techniques based on laparoscopic or endoscopic images." IEEE Signal Processing Magazine. 2010 July. Volume: 27"
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object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and
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S. Kang; J. Paik; A. Koschan; B. Abidi & M. A. Abidi (2003). Tobin, Jr, Kenneth W & Meriaudeau, Fabrice (eds.). "Real-time video tracking using PTZ cameras".
42:. Video tracking can be a time-consuming process due to the amount of data that is contained in video. Adding further to the complexity is the possible need to use
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Video
Tracking provides a comprehensive treatment of the fundamental aspects of algorithm and application development for the task of estimating, over time.
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M. Arulampalam; S. Maskell; N. Gordon & T. Clapp (2002). "A Tutorial on
Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking".
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The image of deformable objects can be covered with a mesh, the motion of the object is defined by the position of the nodes of the mesh.
112:. The motion model is a disruption of a key frame, where each macroblock is translated by a motion vector given by the motion parameters.
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for the robot hand to catch a ball by object tracking with visual feedback that is processed by a high-speed image processing system.
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Object
Tracking by Particle Filtering Techniques in Video Sequences; In: Advances and Challenges in Multisensor Data and Information
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When the target is a rigid 3D object, the motion model defines its aspect depending on its 3D position and orientation.
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Tracking Human
Position and Lower Body Parts Using Kalman and Particle Filters Constrained by Human Biomechanics
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dynamically. Typically the computational complexity for these algorithms is low. The following are some common
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Black, James, Tim Ellis, and Paul Rosin (2003). "A Novel Method for Video
Tracking Performance Evaluation".
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413:"High-speed Catching System (exhibited in National Museum of Emerging Science and Innovation since 2005)"
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Joint IEEE Int. Workshop on Visual
Surveillance and Performance Evaluation of Tracking and Surveillance
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201:: useful for sampling the underlying state-space distribution of nonlinear and non-Gaussian processes.
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Background subtraction is the process by which we segment moving regions in image sequences.
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Parametric & Non-parametric
Background Subtraction Model with Object Tracking for VENUS
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319:. NATO Security Through Science Series, 8. Netherlands: IOS Press. pp. 260–268.
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Proceedings 2nd IEEE and ACM International
Workshop on Augmented Reality (IWAR'99)
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J. Martinez-del-Rincon, D. Makris, C. Orrite-Urunuela and J.-C. Nebel (2010). "
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tracking): an iterative localization procedure based on the maximization of a
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Lyudmila
Mihaylova, Paul Brasnett, Nishan Canagarajan and David Bull (2007).
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466:. Sixth International Conference on Quality Control by Artificial Vision.
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When tracking planar objects, the motion model is a 2D transformation (
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694:". IEEE Transactions on Systems Man and Cybernetics – Part B', 40(4).
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techniques for tracking, a challenging problem in its own right.
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Peter
Mountney, Danail Stoyanov & Guang-Zhong Yang (2010).
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To perform video tracking an algorithm analyzes sequential
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Gyro Stabilized Target Tracker for Off-shore Installation
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localization, as well as filtering and data association.
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Real-time tracking of non-rigid objects using mean shift
176:: detection of object boundary (e.g. active contours or
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94:) of an image of the object (e.g. the initial frame).
714:Camera used to track a ball going through a maze.
442:Ishikawa Watanabe Laboratory, University of Tokyo
417:Ishikawa Watanabe Laboratory, University of Tokyo
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637:. Vol. 1. Addison-Wesley Professional.
552:: CS1 maint: multiple names: authors list (
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631:Emilio Maggio; Andrea Cavallaro (2010).
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708:– Interesting historical example (1980)
38:, traffic control, medical imaging and
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893:3D reconstruction from multiple images
580:IEEE Transactions on Signal Processing
514:Comaniciu, D.; Ramesh, V.; Meer, P., "
149:target representation and localization
144:Target representation and localization
82:Examples of simple motion models are:
913:Simultaneous localization and mapping
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634:Video Tracking: Theory and Practice
438:"Basic Concept and Technical Terms"
364:Kato, H.; Billinghurst, M. (1999).
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978:Automatic number-plate recognition
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983:Automated species identification
968:Audio-visual speech recognition
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661:Karthik Chandrasekaran (2010).
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270:IEEE Signal Processing Magazine
813:Recognition and categorization
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185:Filtering and data association
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26:is the process of locating a
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242:Teknomo–Fernandez algorithm
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973:Automatic image annotation
808:Noise reduction techniques
131:of objects in video frames
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1149:Motion in computer vision
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938:Free viewpoint television
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168:Bhattacharyya coefficient
1003:Computer-aided diagnosis
381:10.1109/IWAR.1999.803809
237:Single particle tracking
16:Not to be confused with
1065:Moving object detection
1055:Medical image computing
818:Research infrastructure
788:Image sensor technology
282:10.1109/MSP.2010.936728
1102:Video content analysis
1070:Small object detection
849:Computer stereo vision
178:Condensation algorithm
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1107:Video motion analysis
918:Structure from motion
864:3D object recognition
156:Kernel-based tracking
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88:affine transformation
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1030:Foreground detection
1013:Reverse image search
993:Bioimage informatics
963:Activity recognition
1097:Autonomous vehicles
1035:Gesture recognition
898:2D to 3D conversion
592:2002ITSP...50..174A
476:2003SPIE.5132..103K
1112:Video surveillance
1050:Landmark detection
958:3D pose estimation
943:Volumetric capture
903:Gaussian splatting
859:Object recognition
773:Commercial systems
375:. pp. 85–94.
164:similarity measure
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44:object recognition
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1045:Image restoration
988:Augmented reality
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933:4D reconstruction
885:3D reconstruction
778:Feature detection
610:10.1109/78.978374
494:10.1117/12.514945
336:978-1-58603-727-7
222:Motion estimation
108:are divided into
102:video compression
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532:: 125–132.
470:: 103–111.
447:12 February
422:12 February
110:macroblocks
32:compression
1143:Categories
798:Morphology
756:Categories
586:(2): 174.
464:Proc. SPIE
248:References
160:mean-shift
120:Algorithms
106:key frames
92:homography
77:frame rate
596:CiteSeerX
534:CiteSeerX
480:CiteSeerX
345:cite book
321:CiteSeerX
232:Swistrack
50:Objective
1159:Tracking
833:Software
793:Learning
783:Geometry
763:Datasets
618:55577025
502:12298526
300:14009451
206:See also
588:Bibcode
472:Bibcode
399:8192877
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614:S2CID
498:S2CID
395:S2CID
369:(PDF)
296:S2CID
266:(PDF)
669:ISBN
639:ISBN
554:link
468:5132
449:2015
424:2015
385:ISBN
351:link
331:ISBN
100:For
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
