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Pedestrian image detection method based on sparse coding and neural network

A neural network, sparse coding technology, applied in the field of image retrieval, can solve the problem of difficult training of Adboost+SVM model

Inactive Publication Date: 2016-06-15
CHINACCS INFORMATION IND
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AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the popular Adboost+SVM model of pedestrian detection is difficult to implement training when the number of samples is large, the present invention provides a pedestrian picture based on sparse coding and neural network that is suitable for a large number of samples, fast detection speed and high accuracy search method

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  • Pedestrian image detection method based on sparse coding and neural network
  • Pedestrian image detection method based on sparse coding and neural network
  • Pedestrian image detection method based on sparse coding and neural network

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Embodiment Construction

[0061] The embodiment of the present invention provides a pedestrian picture detection method based on sparse coding and neural network, such as figure 1 As shown, specifically:

[0062] Step S101: First, image preprocessing is performed on the positive samples and negative samples in the sample data set, and then the aggregated channel features are extracted; the sample data set is the INRIA training sample set and the symmetrical transformation sample set of the INRIA training sample set, and the INRIA training sample set is At present, the most used static pedestrian database has higher definition and can train more accurate classifiers. Among them, the images of positive samples contain pedestrians, and the images of negative samples do not contain pedestrians;

[0063] Step S102: Construct a BP-AdaBoost strong classifier model, and train the BP-AdaBoost strong classifier using the aggregation channel features obtained in step S101;

[0064] Step S103: Obtain the video fr...

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Abstract

The invention discloses a pedestrian image detection method based on sparse coding and a neural network. The method comprises the steps: preprocessing the samples in a sample data set, and extracting the features of an aggregation channel; constructing a BP-AdaBoost strong classifier model and training; preprocessing video frames, and scaling the video frames with different scales to obtain an image pyramid; utilizing the quick image feature pyramid calculating method to integrate with a sliding window detection method to calculate the window images to obtain the features of the aggregation channel, and utilizing the trained strong classifier to perform classification; and when the detection result of the window images is a positive sample, outputting the detection window, and fusing all the detection windows to obtain an accurate positioning window. The pedestrian image detection method based on sparse coding and a neural network is high in the calculating speed and the accuracy when the training samples are large.

Description

technical field [0001] The invention relates to a picture retrieval method, in particular to a pedestrian picture detection method based on sparse coding and a neural network. Background technique [0002] With the advancement of technology, smart devices such as computers are more and more widely used in people's daily life. Computers are more efficient and accurate than humans in handling repetitive and data-intensive tasks. Naturally, people hope that computers can deal with some more intelligent problems like humans. Computer vision is an important part in the new application field of computer. It is the core and the most extensive application of computer vision to replace or assist humans to complete the detection and tracking of targets. From fingerprints or faces used in daily life From unlocking, to automatic driving of cars, robot control, etc. are all closely related to computer vision technology. Human beings are the main body of social life, and the recognition...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/25G06V20/53G06V10/56G06F18/214
Inventor 舒泓新蔡晓东陈昀王爱华
Owner CHINACCS INFORMATION IND
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