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A Front Vehicle Detection Method Based on Shadow Assumption and Hierarchical Hog Symmetry Feature Verification

A symmetrical and shadow technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of slow detection speed, difficult to meet the requirements of rapid detection, and large amount of data.

Active Publication Date: 2016-09-28
HUNAN UNIV
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Problems solved by technology

The amount of data detected in this way is very large, so the detection speed is very slow, and it is difficult to meet the rapidity requirements in the detection process

Method used

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  • A Front Vehicle Detection Method Based on Shadow Assumption and Hierarchical Hog Symmetry Feature Verification
  • A Front Vehicle Detection Method Based on Shadow Assumption and Hierarchical Hog Symmetry Feature Verification
  • A Front Vehicle Detection Method Based on Shadow Assumption and Hierarchical Hog Symmetry Feature Verification

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Embodiment Construction

[0068] The present invention will be further described below in conjunction with the accompanying drawings and examples.

[0069] combine figure 1 , figure 2 , image 3 The basic idea of ​​the present invention is to use the method of hypothesis verification to check the front vehicle, which can be divided into the following three parts: first, manually select a large number of positive and negative sample sets, normalize and calculate its hierarchical HOG symmetric features, and go through extreme learning Machine (ELM) training to obtain a classifier; then the video image collected by the vehicle camera is shaded to obtain the hypothetical vehicle sub-image; finally, the hypothetical sub-image is verified by the classifier to obtain the test result.

[0070] Specifically include the following steps:

[0071] Step 1: Construct a sample library for training, and normalize the sample library;

[0072] The sample library used for training refers to an image library compose...

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Abstract

The invention discloses a preceding car detection method based on shadow hypothesis and layered HOG (histogram of oriented gradient) symmetric characteristic verification. The method includes the steps: manually selecting images shot by a vehicular camera, calculating layered HOG symmetric characteristics of an image library in a normalized manner, and performing training by the aid of an ELM (extreme learning machine) to obtain a classifier; performing shadow processing for the video images acquired by the vehicular camera to obtain hypothetical car sub-graphs; judging the obtained hypothetical sub-graphs by the classifier to obtain detection results. The method can accurately detect preceding cars in real time and has strong robustness for illumination intensity change.

Description

technical field [0001] The invention belongs to the field of machine vision, relates to the field of automobile active safety, in particular to a detection method for a preceding vehicle based on shadow assumption and layered HOG symmetric feature verification. Background technique [0002] With the development of the global economy, the society's demand for transportation continues to grow, and the increase in transportation infrastructure still cannot meet the increase in traffic volume, especially in major cities around the world where economic activities are relatively concentrated, traffic congestion has become a common phenomenon, serious It affects economic development and restricts social activities. The losses caused by traffic accidents are even more shocking. How to avoid traffic accidents has become an important topic in the automotive field, and vehicle detection in front has become a hot research topic. [0003] Existing vehicle detection is mainly feature-ba...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
Inventor 王耀南张楚金卢笑王珂娜刘理吴成中
Owner HUNAN UNIV
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