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A method and device for image fusion quality detection
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A quality detection method and image fusion technology, applied in the field of image processing, can solve problems such as failure to track people, inability to accurately understand the situation of the person being photographed, etc., and achieve the effect of good positioning
Active Publication Date: 2022-01-11
七腾机器人有限公司
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However, artificial inference cannot accurately understand the situation of the subject to a large extent, which leads to the failure of character tracking
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Embodiment 1
[0043] Embodiment 1 of the present application provides an image fusion quality detection method, such as figure 1 shown, including:
[0044] Step 110, searching for the image frame of the tracked person from each frame of the video image;
[0045] Since the embodiment of the present application has high real-time requirements for the tracked person, a deep convolutional neural network model with fast calculation speed is used for image judgment;
[0046] Among them, the image frame of the tracked person is searched from each frame of the video image, specifically: constructing a deep convolutional neural network model, starting from the input layer, and sequentially going through the convolution C 1 layer (output image size [256,256,8]), depthwise convolutional layer D 1 (output image size [128,128,16]), convolutional layer C 2 (output image size [64,64,32]), depth convolutional layer D 2 (output image size [32,32,64]), convolutional layer C 3 (output image size [16,16,1...
Embodiment 2
[0083] Embodiment 2 of the present application provides an image fusion quality detection device, such as Figure 5 shown, including:
[0084] The tracked person's image search module 510 is used to search for the tracked person's image frame from each frame of the video image;
[0085] The image fusion module 520 is used to perform image fusion on multiple tracked person images according to the wavelet transform image fusion method, the contour wavelet fusion method and the scale-invariant feature transform image fusion method;
[0086] The fusion image quality detection module 530 based on the average gradient is used to calculate the average gradient according to the fusion result, and judge the image fusion quality according to the average gradient.
[0087] Wherein, the tracked person image search module 510 is specifically used to construct a deep convolutional neural network model; starting from the input layer, sequentially passing through the first convolutional laye...
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Abstract
The present application discloses an image fusion quality detection method and device. The method includes searching for an image frame of the tracked person from each frame of the video image; performing image fusion on multiple images of the tracked person respectively according to a wavelet transform image fusion method, a contour wavelet fusion method and a scale-invariant feature transformation image fusion method ; Calculate the average gradient according to the fusion results, and judge the image fusion quality according to the average gradient. Using the image fusion quality detection method provided by this application, the quality of image fusion is detected by calculating the average gradient of three different image fusion methods, so as to achieve better positioning of the tracked person.
Description
technical field [0001] The present application relates to the technical field of image processing, in particular to an image fusion quality detection method and device. Background technique [0002] In video security monitoring, the tracking and positioning of people is the most important issue. However, when identifying a person, it is easy to appear that the person's location cannot be easily recognized due to being blocked by an object. [0003] In the prior art, it is generally used to find one or more pictures that can best reflect the face of the person from the video to manually infer the facial features of the person being photographed, so as to realize the person tracking. However, artificial inference cannot accurately understand the situation of the subject to a large extent, which leads to the failure of character tracking. More and more existing technologies have abandoned manual recognition methods and introduced various automatic image recognition methods, b...
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