Video tracking
Video tracking is the process of locating a
Objective
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 frame rate. 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.
Examples of simple motion models are:
- When tracking planar objects, the motion model is a 2D transformation (affine transformation or homography) of an image of the object (e.g. the initial frame).
- When the target is a rigid 3D object, the motion model defines its aspect depending on its 3D position and orientation.
- For video compression, key frames are divided into macroblocks. The motion model is a disruption of a key frame, where each macroblock is translated by a motion vector given by the motion parameters.
- 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.
Algorithms
To perform video tracking an algorithm analyzes sequential
Target representation and localization 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 dynamically.[6] Typically the computational complexity for these algorithms is low. The following are some common target representation and localization algorithms:
- Kernel-based tracking (Bhattacharyya coefficient).
- Contour tracking: detection of object boundary (e.g. active contours or Condensation algorithm). 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.
Filtering and data association 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 behind obstructions.[8] 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.[9] The computational complexity for these algorithms is usually much higher. The following are some common filtering algorithms:
- Kalman filter: 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.[10]
- Particle filter: useful for sampling the underlying state-space distribution of nonlinear and non-Gaussian processes.[11][12][13]
See also
- Match moving
- Motion capture
- Motion estimation
- Optical flow
- Swistrack
- Single particle tracking
- Teknomo–Fernandez algorithm
References
- S2CID 14009451.
- ISBN 978-1-58603-727-7.)
{{cite book}}: CS1 maint: multiple names: authors list (link - S2CID 8192877.
- ^ "High-speed Catching System (exhibited in National Museum of Emerging Science and Innovation since 2005)". Ishikawa Watanabe Laboratory, University of Tokyo. Retrieved 12 February 2015.
- ^ "Basic Concept and Technical Terms". Ishikawa Watanabe Laboratory, University of Tokyo. Retrieved 12 February 2015.
- S2CID 12298526.
- ^ Comaniciu, D.; Ramesh, V.; Meer, P., "Real-time tracking of non-rigid objects using mean shift," Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, vol.2, no., pp. 142, 149 vol.2, 2000
- CiteSeerX 10.1.1.10.3365.)
{{cite journal}}: CS1 maint: multiple names: authors list (link - ^ Gyro Stabilized Target Tracker for Off-shore Installation
- S2CID 55577025.
- ^
Emilio Maggio; Andrea Cavallaro (2010). Video Tracking: Theory and Practice. Vol. 1. Addison-Wesley Professional. ISBN 9780132702348.
Video Tracking provides a comprehensive treatment of the fundamental aspects of algorithm and application development for the task of estimating, over time.
- ^
Karthik Chandrasekaran (2010). Parametric & Non-parametric Background Subtraction Model with Object Tracking for VENUS. Vol. 1. ISBN 9780549524892.
Background subtraction is the process by which we segment moving regions in image sequences.
- ^ J. Martinez-del-Rincon, D. Makris, C. Orrite-Urunuela and J.-C. Nebel (2010). "Tracking Human Position and Lower Body Parts Using Kalman and Particle Filters Constrained by Human Biomechanics". IEEE Transactions on Systems Man and Cybernetics – Part B', 40(4).
External links
- – Interesting historical example (1980) of Cromemco Cyclops Camera used to track a ball going through a maze.