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Fast adaptation method for traffic video monitoring target detection based on machine vision

A technology for video surveillance and target detection, applied in instruments, computer parts, character and pattern recognition, etc., can solve problems such as large time and labor costs, affecting the wide application of traffic video intelligent detection technology, and difficulty in realizing it.

Inactive Publication Date: 2013-07-17
HUNAN HUANAN OPTO ELECTRO SCI TECH CO LTD
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  • Application Information

AI Technical Summary

Problems solved by technology

Such a process requires a lot of time and labor costs, and it is difficult to obtain a comprehensive sample library. The current situation has seriously affected the wide application of traffic video intelligent detection technology.

Method used

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  • Fast adaptation method for traffic video monitoring target detection based on machine vision
  • Fast adaptation method for traffic video monitoring target detection based on machine vision
  • Fast adaptation method for traffic video monitoring target detection based on machine vision

Examples

Experimental program
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Embodiment Construction

[0037]The traffic video monitoring system is composed of three parts: camera, monitoring equipment and background server, and its working method is as follows:

[0038] The traffic video captured by the camera is transmitted to the monitoring device and the background server, and two initial classifiers with good detection performance are trained, which are used in the monitoring device to detect the traffic video surveillance target in the actual scene, and are carried out on the background server at the same time The update of the classifier, so the initial classifier must not only ensure that the initial monitoring process can provide high-precision detection results, but also ensure the credibility of the sample labels newly added to the sample library during the co-training semi-supervised learning process .

[0039] In the monitoring equipment, these two classifiers are used to detect the traffic video surveillance target in the initial stage. When the detection results ...

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PUM

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Abstract

The invention belongs to the field of machine vision and intelligent control for achieving fast self-adaptation of traffic video monitoring target detection. The method comprises the steps of building an initial training sample bank; training an AdaBoost classifier based on Haar characteristics and a support vector machine (SVM) classifier based on histograms of oriented gradients (HOG) characteristics respectively; and detecting monitoring images frame by frame by employing of the two classifiers, wherein the detection process is divided into steps of predicting of sub-images in a detection frame through the two classifiers respectively, performing of confidence-degree determination on predicted results, adding of prediction labels corresponding to large confidence-degree and the sub-images into additional training sample banks of the classifiers corresponding to small confidence-degree till the size of the detection frame reaches half the sizes of detected images, retraining of the two classifiers by using the updated training sample banks and detecting of a next frame of image till all images are detected, and the final classifiers can be used for detecting targets of vehicles, pedestrians and the like in actual traffic scenes.

Description

technical field [0001] The invention belongs to the field of machine vision and intelligent control, and is an invention for detecting traffic video monitoring targets by using computer technology, image processing technology, machine learning technology and pattern recognition technology. Background technique [0002] Accurate and real-time detection of monitoring targets such as pedestrians and vehicles in traffic videos is the fundamental guarantee for subsequent traffic video processing such as target tracking and behavior analysis. There have been many related researches on object detection in traffic video surveillance, and most of them currently use machine learning methods in pattern recognition for object detection. As long as the target image to be detected is added to the positive sample library of the training sample library, the complex background is added to the negative sample library of the training sample library, an appropriate classifier is selected to lea...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 刘星辛乐杨德亮陈阳舟吴旭
Owner HUNAN HUANAN OPTO ELECTRO SCI TECH CO LTD
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