Particle Filter Video Image Tracking Method Based on Dual Model
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A video image and particle filter technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as robust algorithms, and achieve stable tracking effects and strong robustness
Inactive Publication Date: 2016-04-20
SHENYANG UNIV
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However, there is no more robust algorithm for objects with obvious geometric deformation, or when the object undergoes strong illumination transformation.
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Embodiment 1
[0030] The geometrically deformable target is tracked using the algorithm described above.
[0031] Step 1: The video image sequence has a total of 271 frames, and the size of each frame is 320*240. The initial size of the template is 42*42. 8-dimensional vector is the projection transformation parameter of tracking boundary shape, t=1;
[0032] Step 2: Predict according to the following formula ,j=1,2....16.16 is the number of sampled particles; v is the velocity vector of the state transition from time t-1 to time t.
[0033]
[0034] For example to get:
[0035] Step 3: Use the following formula to construct the covariance matrix, and calculate the correlation with each The covariance of the corresponding image patch ;
[0036]
[0037] For a given region R whose size is 42×42, , is the mean vector. . x,y Indicates the abscissa and ordinate of the corresponding pixel. and represent images respectively exist x direction and y The gradient val...
Embodiment 2
[0064] Light transform targets are tracked using the algorithm described above.
[0065] Step 1: The video image sequence has a total of 600 frames, and the size of each frame is 320*240. The initial size of the template is 104*110. 8-dimensional vector is the projection transformation parameter of tracking boundary shape, t=1;
[0066] Step 2: Predict according to the following formula ,j=1,2...25.25 is the number of sampled particles; v is the velocity vector of the state transition from time t-1 to time t.
[0067]
[0068] For example to get:
[0069] Step 3: Use the following formula to construct the covariance matrix, and calculate the correlation with each The covariance of the corresponding image patch ;
[0070]
[0071] For a given region R, its size is 104*110, , is the mean vector. . x,y Indicates the abscissa and ordinate of the corresponding pixel. and represent images respectively exist x direction and y The gradient value in the...
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Abstract
The invention discloses a particle filtering video image tracking method based on a dual model, and relates to video image tracking methods. A dual particle filter is built and alternating tracking dynamic models are achieved. One of the particle filters is represented in on-line updating goals of a covariance matrix Riemann flow model and the other one of the particle filters is used for conducting tracking to bounding box parameters of objects in a projection transformation group. Lie algebra and transformation relation of a tangent space of the Lie algebra are adopted by measuring of the filtering parameters, considering the fact that the essence of the process of target-orient imaging is the process of projection transformation, when the particle filtering video image tracking method builds a tracking algorithm based on the particle filtering, a covariance flow model and a projection transformation group (SL (3) group) are combined simultaneously. The particle filtering video image tracking method based on the dual model has the advantages of achieving stable tracking to object with obvious geometric transformation, achieving stable tracking under the condition that changing of light is large, and being good in effectiveness and robustness.
Description
technical field [0001] The invention relates to a video image tracking method, in particular to a dual model-based particle filter video image tracking method. Background technique [0002] In recent years, many algorithms use the feature covariance matrix as the regional characteristics to describe the image, and use the particle filter method to use a group of random samples with weights to represent the current density of the state to achieve target tracking. And affine transformation is used to describe the apparent change of the target. However, there is no more robust algorithm for objects with obvious geometric deformation, or when the object undergoes strong illumination changes. Contents of the invention [0003] The object of the present invention is to provide a particle filter video image tracking method based on dual models. This method not only achieves stable tracking for targets with obvious geometric deformation, but also can achieve stable tracking unde...
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