Image processing method and device and processing equipment

An image processing and image technology, applied in the field of image recognition, which can solve problems such as difficulty, difficulty in realization, and different infrared intensities

Active Publication Date: 2019-05-10
BEIJING KUANGSHI TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to factors such as high hand deformation, serious hand self-occlusion and external occlusion, complex background noise, variable shooting angles, light intensity, and infrared intensity, gesture recognition for infrared cameras is already very difficult, especially The recognition of multiple hands cannot be processed in real time or at the same time, and real-time gesture recognition is also limited by computing resources, etc., which will be more difficult to achieve
[0003] For the above-mentioned problems of image recognition in the prior art, no effective solution has been proposed yet

Method used

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  • Image processing method and device and processing equipment
  • Image processing method and device and processing equipment
  • Image processing method and device and processing equipment

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Embodiment 1

[0029] First, refer to figure 1 The processing device 100 for implementing the embodiments of the present invention will be described, and the processing device can be used to run the methods of the various embodiments of the present invention.

[0030] Such as figure 1 As shown, the processing device 100 includes one or more processors 102, one or more memories 104, an input device 106, an output device 108, and a data collector 110, and these components are connected through a bus system 112 and / or other forms ( not shown) interconnection. It should be noted that figure 1 The components and structure of the processing device 100 shown are only exemplary and not limiting, and the processing device may also have other components and structures as required.

[0031] The processor 102 may be implemented in at least one hardware form of a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic array (PLA) and an ASIC (Application Specific I...

Embodiment 2

[0038] An embodiment of the present invention provides an image processing method, see figure 2 A flow chart of an image processing method is shown, the method can be executed by the processing device provided in the foregoing embodiment, and the method can include the following steps:

[0039] Step S202, acquiring an image to be recognized.

[0040] For example, if the image to be recognized is an infrared image, an image of the target to be recognized may be collected by an infrared camera, thereby obtaining the image to be recognized.

[0041] Step S204, inputting the image to be recognized into the target recognition network.

[0042] Wherein, the target recognition network includes a feature pyramid neural network with multiple convolutional calculation layers and multiple residual calculation layers connected in sequence. The convolution calculation layer includes convolution blocks, and the residual calculation layer includes residual blocks. The residual block incl...

Embodiment 3

[0064] An embodiment of the present invention provides a target recognition network, including: a backbone network and a branch network.

[0065] The above-mentioned backbone network is a feature pyramid neural network, including a plurality of sequentially connected convolution calculation layers and residual calculation layers, each of which includes at least one convolution block, and each of which includes at least one residual bad block.

[0066]Wherein, the residual block includes at least two sequentially connected convolution blocks, the convolution block includes at least one channel invariant convolution layer, and the channel invariant convolution layer refers to the overall input feature when calculating the input feature and output feature channels remain unchanged. The channel-invariant convolutional layer is used to perform convolution calculation on the input feature map to obtain the output feature map. Specifically, the convolution layer can separately perfo...

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Abstract

The invention provides an image processing method and device and processing equipment, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a to-be-recognized image; inputting the image to be identified into a target identification network; wherein the target identification network comprises a plurality of convolution calculation layers and a plurality of residual error calculation layers which are connected in sequence; wherein the convolution calculation layer comprises a convolution block, and the residual calculation layer comprises a residual block; The residual block comprises at least two convolution blocks which are sequentially connected; The convolution block comprises at least one channel invariable convolution layer; when the channel invariant convolution layer calculates the input feature map, each channel of the input feature map is independently subjected to convolution transformation to obtain one channel of the output feature map; and performing posture recognition on the to-be-recognized image through the target recognition network to obtain a posture recognition result, the posture recognition result comprising the position and the mode of the target contained in the to-be-recognized image. According to the embodiment of the invention, the calculation amount can be reduced, the receptive field is increased, and the position and mode are accurately determined.

Description

technical field [0001] The present invention relates to the technical field of image recognition, in particular to an image processing method, device and processing equipment. Background technique [0002] Gesture recognition is the pillar technology of touchless human-computer interaction without the help of mechanical devices such as touch screens. Whether gesture recognition is real-time or not determines the popularity of touchless human-computer interaction. However, due to factors such as high hand deformation, serious hand self-occlusion and external occlusion, complex background noise, variable shooting angles, light intensity, and infrared intensity, gesture recognition for infrared cameras is already very difficult, especially The recognition of multiple hands cannot be processed in real time or at the same time, and real-time gesture recognition is also limited by computing resources, which will be even more difficult to achieve. [0003] For the above-mentioned ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
Inventor 陈文科姚聪孙晨
Owner BEIJING KUANGSHI TECH
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