Method and system for achieving classification of pedestrians and vehicles based on neural network

A technology of neural network and convolutional neural network, which is applied in the field of automobile classification to achieve the effect of improving accuracy and speed of classification

Inactive Publication Date: 2015-04-08
GUANGZHOU INST OF ADVANCED TECH CHINESE ACAD OF SCI
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Problems solved by technology

The disadvantage of this method is that the polymorphism of pedestrians and the diversity of people and vehicles determine that ideal results cannot be achieved through model matching.
[0019] Howeve

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  • Method and system for achieving classification of pedestrians and vehicles based on neural network
  • Method and system for achieving classification of pedestrians and vehicles based on neural network
  • Method and system for achieving classification of pedestrians and vehicles based on neural network

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[0057] The invention provides a method and system for classifying people and vehicles based on a neural network, aiming at effectively and accurately classifying people and vehicles in a complex environment, thereby improving the classification accuracy and classification speed. The present invention can be applied to a large number of occasions, such as traffic monitoring systems, security equipment systems, and the like. It has higher accuracy in human-vehicle classification and faster detection speed, which is crucial for some real-time systems and has huge application prospects.

[0058] like figure 1 As shown, the method for realizing the classification of people and vehicles based on a neural network provided by the present invention includes the following steps:

[0059] S100, collecting several training samples, and using a convolutional neural network to classify the training samples to obtain a classifier including label results;

[0060] S200. When classifying peo...

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Abstract

The invention relates to the technical field of classification of pedestrians and vehicles, and discloses a method and a system for achieving the classification of pedestrians and vehicles based on a neural network. The method includes the following steps of collecting a plurality of training samples, classifying the training samples by a convolutional neural network, and thereby obtaining a classifier including tab results, when the pedestrians and the vehicles are classified, reading a video image to be detected, detecting moving objects in the image, and processing the image in blocks according to the moving objects; and then classifying the image blocks by the classifier to obtain a detection result; therefore, the neural network system can be simply constructed as the classifier; the system is trained by using different pedestrian and vehicle samples such that the system automatically learns the complex class conditional density of the samples, and problems caused by an artificial hypothesis class conditional density function are avoided. Compared with existing methods for classifying pedestrians and vehicles, the method for achieving the classification of pedestrians and vehicles based on the neural network has the advantages of improving classifying accuracy as well as classifying speed.

Description

technical field [0001] The invention relates to the technical field of vehicle classification, in particular to a method and system for realizing the classification of people and vehicles based on a neural network. Background technique [0002] Traffic accidents are one of the main factors leading to the death of pedestrians. Because cyclists and pedestrians are often in a vulnerable position in traffic accidents, once they have a traffic accident with a motor vehicle, they are easily injured. Therefore, pedestrian detection technology has become a research direction that has attracted much attention in the field of intelligent analysis in recent years. Especially for the field of intelligent traffic video analysis, the classification and detection of objects play a vital role in road management and traffic safety. [0003] At present, the main methods of target classification are methods based on shape model matching, methods based on classifiers and methods using gradient ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/02
CPCG06F18/2411
Inventor 冷斌贺庆官冠胡欢蒋东国
Owner GUANGZHOU INST OF ADVANCED TECH CHINESE ACAD OF SCI
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