Pedestrian Detection Method Based on Self-learning

A pedestrian detection and self-learning technology, applied in the field of pedestrian detection based on self-learning, can solve problems such as inability to adapt to specific tasks

Active Publication Date: 2020-04-24
HEFEI NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Purpose of the invention: In order to overcome the inability to adapt to specific tasks in the prior art, provide a pedestrian detection method based on self-learning, which can adapt to specific scenarios with any offline-trained pedestrian classifier, and achieve a better recognition rate

Method used

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  • Pedestrian Detection Method Based on Self-learning
  • Pedestrian Detection Method Based on Self-learning

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0053] like figure 1 As shown, the present invention provides a pedestrian detection method based on self-learning, comprising the following specific steps: first train a cascade classifier based on AdaBoost as an offline classifier, and simultaneously use a group of public pedestrian photos to train a Gaussian mixture model, feature encoding Using HOG features and location information, and then using a low-threshold offline classifier to detect pedestrians in specific scenes, and output the confidence scores of candidate objects, then select high confidence scores as positive samples, low confidence scores as negative samples, and use Gaussian The hybrid model re-represents the candidate detection objects, and finally uses the SVM classifier to train a discriminative pedestrian classifier online to re-predict the candidate objects and output probability estimates.

[0054] The offline classifier described in the above steps: the training data comes from any pedestrian data se...

Embodiment 2

[0059] like figure 2 As shown, the offline classifier is trained using the cascade classifier in OpenCV, including two parts: data set preparation and running training program, wherein the data set is prepared as training data: use opencv_createsamples to create a set of positive samples, and manually prepare a large number of negative samples. Sample picture; run the training program to train the cascade classifier: set the feature type to LBP. Considering that the training and detection speed of the LBP feature is several times faster than that of the Haar feature, the LBP feature is used to train the cascade classifier using opencv_traincascade.

Embodiment 3

[0061] like image 3 As shown, the Gaussian mixture model includes two parts of feature encoding and GMM training, specifically:

[0062] Feature encoding: For each pedestrian picture in the INRIA dataset, a three-layer Gaussian pyramid is first constructed, and then overlapping image blocks are extracted from each layer of the image pyramid. Assume that each pedestrian picture includes N image blocks Extract the HOG feature hog of each image block pi and its location information l pi =[xy] T , and finally the feature encoding of each image block is f pi =[hog pi T , l pi T ] T , all image blocks constitute pedestrian sample features

[0063] GMM training: A Gaussian mixture model trained offline can be expressed as

[0064]

[0065] Among them, K is the number of Gaussian mixture components, I is the identity matrix, is the mixture weight composed of the kth Gaussian, is a Gaussian distribution with mean μ k and the variance is f is the pedestrian sa...

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Abstract

The invention discloses a pedestrian detection method based on self-learning, which includes the following specific steps: first, train a cascaded classifier based on AdaBoost as an offline classifier, and simultaneously use a group of public pedestrian photos to train a Gaussian mixture model, and use HOG for feature encoding Feature and location information, then use a low-threshold offline classifier to detect pedestrians in specific scenes, and output the confidence scores of candidate objects, then select high confidence scores as positive samples, low confidence scores as negative samples, and use Gaussian mixture model Re-represent the candidate detection objects, and finally use the SVM classifier to train a discriminative pedestrian classifier online to re-predict the candidate objects and output probability estimates. The invention solves the problem that the traditional pedestrian detection method cannot adapt to a specific scene, and has a certain promotion effect on the pedestrian detection technology in the specific scene. The invention has significantly improved the recognition rate compared with the traditional pedestrian detection method.

Description

technical field [0001] The invention relates to a pedestrian detection method in the field of intelligent transportation, in particular to a pedestrian detection method based on specific scene self-learning. Background technique [0002] The problem of road traffic safety has seriously affected economic development and social construction. Reducing the occurrence of road traffic accidents and casualties is an important issue related to people's livelihood. Road traffic safety issues are affected by multiple factors such as pedestrians, vehicles, and roads. Since pedestrians are the main participants and vulnerable persons in road traffic, ensuring pedestrian safety is the key to road safety issues and an important aspect in the field of intelligent transportation systems. Task. [0003] The pedestrian detection method is the core supporting technology of the intelligent transportation system, which has a profound impact on ensuring the safety of pedestrians and reducing the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/103G06F18/2411
Inventor 施培蓓曹风云胡玉娟杨雪洁王璐钱言玉王筱薇倩张娜谢超吴友情
Owner HEFEI NORMAL UNIV
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