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Moving target detection method on basis of pulse coupled neural network

A technology of pulse coupled neural and moving target detection, applied in biological neural network models, image data processing, instruments, etc., can solve problems such as poor robustness of dynamic backgrounds and difficult stability of local features.

Active Publication Date: 2014-04-02
NORTHEASTERN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The limitation of this method is that because it fails to make good use of the global features of the image, it can only effectively suppress the background disturbance when the dynamic background motion is limited to a local area, and it is not suitable for dynamic background environments with violent motion. (Limited to the local area mentioned here means limited to the area where the local features are calculated
Qualitatively speaking, when the dynamic background moves in the area where the local features are calculated, the calculated local area features tend to be stable; when the dynamic background moves beyond the area where the local features are calculated, the obtained local features are not easy to be stable. The background model established by it is also less robust to the dynamic background of violent motion)

Method used

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  • Moving target detection method on basis of pulse coupled neural network
  • Moving target detection method on basis of pulse coupled neural network
  • Moving target detection method on basis of pulse coupled neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] Embodiment 1 of the present invention: a moving target detection method based on a pulse-coupled neural network, such as image 3 shown, including the following steps:

[0042] a. Utilize the pulse-coupled neural network to sense the video image sequence and extract the global features of the video image; wherein, a neuron of the pulse-coupled neural network corresponds to a pixel of the video image; for the neural network located at (i, j) in the pulse-coupled neural network element, which receives external stimulus information S at time n ij and the pulse information of other neurons in the adjacent k×l neighborhood at n-1 time {Y kl} After the impact, the feedback input F ij , Linear connection input L ij , internal activity item U ij , membrane potential dynamic threshold θ ij , the pulse output Y of the pulse generator ij And the global feature Q of the extracted video image ij They are:

[0043] f ij (n)=S ij ;

[0044] L ...

Embodiment 2

[0063] Embodiment 2: the moving target detection method based on pulse coupled neural network, such as image 3 shown, including the following steps:

[0064] a. Utilize the pulse-coupled neural network to sense the video image sequence and extract the global features of the video image; wherein, a neuron in the pulse-coupled neural network corresponds to a pixel in the video image; for the pulse-coupled neural network located at (i, j) neurons, which receive external stimulus information S at time n ij And the pulse information of other neurons in the adjacent k×l neighborhood at n-1 time {Y kl} After the impact, the feedback input F ij , Linear connection input L ij , internal activity item U ij , membrane potential dynamic threshold θ ij , the pulse output Y of the pulse generator ij And the global feature Q of the extracted video image ij They are:

[0065] f ij (n)=S ij ;

[0066] L ij ( n ...

experiment example

[0086] Experimental example: figure 1 is a frame image in a set of video images ( figure 1 In the figure, the coat 1 of the character is purple, the shirt 2 is green, the hair 3 is black and yellow, the branch 4 on the left is green, the branch 5 on the right is black, the wall brick 6 of the building is earthy yellow, and the wall brick 6 has The sun shines through the branches 7, the sky 8 is blue), the monitoring background of this group of video images is dynamic and changeable because it contains branches swaying with the wind, which brings difficulties to the detection of moving objects.

[0087] Using the method of the present invention to figure 1 Carry out moving object detection, specifically include the following steps:

[0088] (1-1) Use the pulse-coupled neural network to extract the global features of the image: for images figure 1 Such a group of 120×160 color video images can be perceived by a neural network composed of 120×160 pulse-coupled neurons; for the...

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Abstract

The invention discloses a moving target detection method on the basis of a pulse coupled neural network, which comprises the following steps of a, sensing a video image sequence by utilizing the pulse coupled neural network and extracting global features of a video image; b, establishing a global feature histogram of each pixel; c, for each pixel, utilizing the global feature histograms corresponding to first K frames to establish an initial background model of the pixel; d, for each pixel, calculating similarity of a global feature histogram of a current frame image and the corresponding global feature histogram in the background model and detecting whether the pixel is a moving target; e, for each pixel, utilizing the global feature histogram of the current frame image to update the background model of the pixel. The moving target detection method uses the integral characteristics of human visual perception for reference, utilizes the pulse coupled neural network to extract the global features of the image and is beneficial for inhibiting the negative influence of disturbance of a dynamic background on detection of the moving target so as to improve accuracy of detecting the moving target.

Description

technical field [0001] The invention relates to a moving target detection method based on a pulse-coupled neural network, belonging to the technical field of video image processing. Background technique [0002] In the intelligent video surveillance system, moving target detection technology is the basis of other post-processing (such as target tracking, target recognition and behavior analysis, etc.). In order to accurately and effectively segment moving target areas in various monitoring environments, it is of great significance to study moving target detection methods that are robust to complex dynamic scenes (such as illumination changes, background disturbances, etc.). [0003] In order to solve the difficulties brought by complex dynamic scenes to moving target detection, at present, people mainly use the local neighborhood features of pixels to describe the background model of pixels, and extract the local area features of the image to improve the feature for the area...

Claims

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Application Information

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IPC IPC(8): G06T7/20G06T5/50G06N3/02
Inventor 汪晋宽才溪韩光
Owner NORTHEASTERN UNIV
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