Target positioning method and device based on convolution neural network

A convolutional neural network, target positioning technology, applied in the field of machine vision, can solve the problems of lack of universality, low accuracy, limited and so on

Active Publication Date: 2016-04-20
ZHEJIANG UNIVIEW TECH CO LTD
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Problems solved by technology

[0004] The present invention provides a method and device for target positioning based on convolutional neural network to solv

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  • Target positioning method and device based on convolution neural network
  • Target positioning method and device based on convolution neural network
  • Target positioning method and device based on convolution neural network

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Embodiment Construction

[0021] In order to facilitate the understanding of the present invention, the following will further explain and illustrate with specific embodiments in conjunction with the accompanying drawings, and the embodiments do not constitute a limitation to the protection scope of the present invention.

[0022] The technical solutions provided by the embodiments of the present invention can be applied to the field of machine vision technology, and can be applied to multi-target or single-target positioning of motor vehicles, bicycles, pedestrians, etc. in intelligent traffic scenarios, and can also be applied to targets in public security image investigations Objects, such as the positioning of hats, umbrellas, etc., and the positioning of certain specific targets in the pan-bayonet intelligent analysis technology.

[0023] The training samples and test samples mentioned in this application document are foreground pictures, which may contain more background; while the positive sample...

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Abstract

The present invention provides a target positioning method and device based on a convolution neural network. The method comprises a step of extracting the CNN feature of a training sample, a step of using the CNN feature to train an SVM classifier to obtain a first-class classifier, a step of training the SVM classifier by using the first-class classifier and the CNN features of the image zoomed in different scales by the training sample to obtain a second-class classifier, a step of extracting the CNN feature of the training sample, carrying out detection by using the first-class classifier, and obtaining multiple test target frames and corresponding first-class scores, a step of using the second-class classifier to grade remaining target testing frames with maxima suppression, and obtaining the second-class score of each remaining target test frame, and a step of carrying out weighted processing on the first-class score and the second-class score of each remaining target test frame, and sorting each remaining target test frame. The application of the target positioning method to carry out target positioning is not limited by a scene, and the accuracy of the target positioning is high.

Description

technical field [0001] The present invention relates to the technical field of machine vision, in particular to a method and device for locating a target based on a convolutional neural network. Background technique [0002] Target positioning is an important technology in machine vision. After locating the target, the system can easily store, analyze, 3D model, identify, track and search for the target. Therefore, the accuracy of target positioning directly affects the accuracy of the target. The effect of target analysis, recognition, tracking and search. [0003] In the prior art, some interference factors are generally eliminated by preprocessing the image, such as denoising the image by filtering, image enhancement, and quantization; The Gabor texture feature of the face, as well as the FAST matching algorithm and SIFT (Scale-invariant feature transform, scale-invariant feature transform) and other methods perform feature extraction on the preprocessed image to obtain ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/06
CPCG06N3/06G06F18/2411
Inventor 王智玉
Owner ZHEJIANG UNIVIEW TECH CO LTD
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