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Multi-target video tracking system based on fractal feature estimation

A technology of video tracking and fractal features, applied in the field of video tracking, can solve problems such as inability to track the initial image sequence, and achieve the effects of low missed detection rate, good robustness, and high tracking stability

Inactive Publication Date: 2019-11-12
CHANGSHU INSTITUTE OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage is that this method uses the initially set image sequence to calculate the complete information of the target after background elimination, and cannot effectively track the initial image sequence.
However, the observation model established by using the HSV color histogram is only based on the distance between the models

Method used

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  • Multi-target video tracking system based on fractal feature estimation
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  • Multi-target video tracking system based on fractal feature estimation

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Experimental program
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Embodiment 1

[0055] A multi-target video tracking system based on fractal feature estimation, including image local fractal feature estimation, observation model establishment, likelihood function calculation and multi-Bernoulli filter tracking.

[0056] Such as figure 1 As shown, the system consists of three main modules: image conversion module based on fractal feature estimation, observation model and likelihood function calculation module, and sequential Monte Carlo multi-Bernoulli filter module.

[0057] For each frame of original image in video original image sequence, such as figure 2 The cell sequence shown, as image 3 The indoor scene shown, such as Figure 4 In the outdoor scene shown, firstly, the fractal feature of each frame of image is calculated by using the rescaled range method, that is, the Hurst index, and it is converted into a new Hurst index image, and the image is used as a sequential Monte Carlo multi-Bernoulli input to the filter, which then performs tracking ...

Embodiment 2

[0080] A multi-target video tracking system based on fractal feature estimation, including image local fractal feature estimation, observation model establishment, likelihood function calculation and multi-Bernoulli filter tracking.

[0081] The fractal feature estimation uses the rescaled range method to estimate the Hurst exponent of all pixels in each frame image, the steps are:

[0082] The given time series {x 1 ,x 2 ,...,x m ,...,x N} into t non-overlapping subintervals, and then calculate the tth subinterval (x 1 ,x 2 ,...,x t ) mean

[0083]

[0084] Using this mean value, the cumulative dispersion φ is calculated sequentially t (i), range R t and standard deviation S t .

[0085]

[0086]

[0087]

[0088] Calculate R for all subintervals in turn t / S t , R for any subinterval t / S t satisfy

[0089] (R / S) t ∝t H (5)

[0090] Among them, H is the Hurst exponent, and in the double logarithmic coordinate system, H is the point (logt,log(R ...

Embodiment 3

[0112] A multi-target video tracking system based on fractal feature estimation, including image local fractal feature estimation, observation model establishment, likelihood function calculation and multi-Bernoulli filter tracking.

[0113] Among them, the observation model establishment and likelihood function calculation, steps:

[0114] Assuming that the Hurst index image of size M×N in the kth frame is expressed as in is the Hurst exponent of the i-th pixel. The multi-target state is expressed as X={x 1 ,x 2 ,...,x n}, where n is the number of targets, and the i-th target x i Expressed as a rectangular block T(x i ). Modify the measurement observation model (14) to (15).

[0115]

[0116]

[0117] where g(y) represents the likelihood function that the pixel belongs to the background, g(y; x i ) is the likelihood function of the i-th target in the image y, g(H k |X) represents the kth frame Hurst index image H k The likelihood function of the target stat...

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Abstract

The invention discloses a multi-target video tracking system based on fractal feature estimation. The multi-target video tracking system comprises an image local fractal feature estimation module, anobservation model establishment and likelihood function calculation module and a multi-Bernoulli filter tracking module. The local fractal feature estimation module calculates local fractal features of each frame of image and forms a new Hurst index image. The observation model establishing and likelihood function calculating module is used for establishing an observation model and calculating a likelihood function by utilizing local fractal characteristics and a Hurst index. The multi-Bernoulli filter tracking module is used for tracking a plurality of targets in the Hurst index image. According to the method, under the condition that the sensor is static or moves, a plurality of targets can be correctly tracked, and good robustness is achieved for complex tracking such as target enteringand leaving, target part shielding and target number changing.

Description

technical field [0001] The invention relates to the technical field of video tracking, in particular to a multi-target video tracking system based on fractal feature estimation. Background technique [0002] Multi-object video tracking is a hot issue in the field of computer vision applications. At present, multi-target video tracking methods can be roughly divided into three categories. The first type is called the detection-based video tracking method, which is to detect the target in each frame of the video, and then use the method of data association or labeling to distinguish and classify the target, but this kind of method is easy to lose the target and output too much. Many false alarm targets. The second type is called the hybrid method of detection and filtering, which uses filtering algorithms to process detected and undetected target information. Such methods also need to use data association methods to determine the target state. The third category is based o...

Claims

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Application Information

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IPC IPC(8): G06T7/277
CPCG06T2207/10016G06T7/277
Inventor 朱继红徐本连鲁明丽朱培逸
Owner CHANGSHU INSTITUTE OF TECHNOLOGY
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