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Image processing method, device and system, storage medium and computing equipment

An image processing and image technology, which is applied in the directions of image data processing, calculation, image analysis, etc., can solve the problems of limited quantity, high cost of data and label acquisition, and high acquisition cost, so as to ensure the accuracy of processing and reduce the cost of acquisition.

Pending Publication Date: 2021-10-12
ALIBABA GRP HLDG LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to solve the above problems, it can be achieved by training the corresponding algorithm model. However, due to the high cost and limited number of low-frequency events or image annotations, the algorithm model cannot be trained with sufficient data, and the processing accuracy of the algorithm model is relatively low. Low
[0004] Aiming at the high cost of obtaining the target image by directly acquiring the image or labeling the image in related technologies, no effective solution has been proposed yet.

Method used

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  • Image processing method, device and system, storage medium and computing equipment
  • Image processing method, device and system, storage medium and computing equipment
  • Image processing method, device and system, storage medium and computing equipment

Examples

Experimental program
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Embodiment 1

[0038] According to an embodiment of the present application, an image processing method is provided. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the steps shown in the flow chart Although a logical order is shown, in some cases the steps shown or described may be performed in an order different from that shown or described herein.

[0039] The method embodiments provided in the embodiments of the present application may be executed in mobile terminals, computer terminals or similar computing devices. figure 1 A hardware structural block diagram of a computer terminal (or mobile device) for implementing the image processing method is shown. Such as figure 1As shown, the computer terminal 10 (or mobile device 10) may include one or more (shown by 102a, 102b, ..., 102n in the figure) processor 102 (the processor 102 may include but not ...

Embodiment 2

[0082] According to an embodiment of the present application, an image processing method is also provided.

[0083] Figure 7 is a flow chart of the second image processing method according to the embodiment of the present application. Such as Figure 7 As shown, the method includes the following steps:

[0084] Step S602, acquiring the first image containing the target event;

[0085] The target events in the above steps can be events with low occurrence frequency in traffic video surveillance scenarios, such as tunnel fire, two-vehicle collision, two-vehicle rear-end collision, scratching, etc., or events with high labeling costs, such as video images The depth of field of , but not limited to, can also be other events, such as common events such as vehicle congestion, vehicle suspension, etc.

[0086] Step S604, acquiring the collected second image;

[0087] The second image in the above steps can be a real scene image that is actually collected, and the image can be d...

Embodiment 3

[0104] According to an embodiment of the present application, an image processing device for implementing the above image processing method is also provided, such as Figure 8 As shown, the device 700 includes: an image acquisition module 702 and a processing module 704 .

[0105] Wherein, the image acquisition module 702 is used to acquire the first image containing the target event; the processing module 704 is used to process the first image with the adversarial generation network to obtain the target image, wherein the adversarial generation network uses the acquisition The attribute parameters of the target image are the same as the attribute parameters of the second image, and the target image includes the target event.

[0106] It should be noted here that the above-mentioned image acquisition module 702 and processing module 704 correspond to Step S202 to Step S204 in Embodiment 1, and the examples and application scenarios realized by the two modules are the same as t...

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Abstract

The invention discloses an image processing method, device and system, a storage medium and computing equipment. The method comprises the following steps: acquiring a first image containing a target event; and processing the first image by using a generative adversarial network to obtain a target image, the generative adversarial network being obtained by training a collected second image, the attribute parameter of the target image being the same as the attribute parameter of the second image, and the target image including the target event. According to the method and the device, the technical problem that the acquisition cost of data and annotations is relatively high due to the fact that the target image is obtained by directly acquiring the image or annotating the image in the prior art is solved.

Description

technical field [0001] The present application relates to the field of traffic video surveillance, in particular, to an image processing method, device and system, storage medium, and computing equipment. Background technique [0002] In existing traffic video surveillance scenarios, traffic event detection is mainly aimed at common event detection, such as vehicle congestion, vehicle suspension, etc. Wipe, etc.) for testing. However, low-frequency events or image annotation (for example, the depth of field of video images) have certain specific requirements, such as tunnel fire detection, specific accident detection and classification, and physical space mapping of video images. [0003] In order to solve the above problems, it can be achieved by training the corresponding algorithm model. However, due to the high cost and limited number of low-frequency events or image annotations, the algorithm model cannot be trained with sufficient data, and the processing accuracy of ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06T7/00H04N7/18
CPCG06T7/0002G06N3/08H04N7/18G06T2207/10016G06T2207/30204G06T2207/20081G06T2207/20084G06N3/045G06F18/241
Inventor 范托高强华
Owner ALIBABA GRP HLDG LTD
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