Pedestrian image feature classification method and system

Abstract

The invention provides a pedestrian image feature classification method and system, and the method comprises the steps: carrying out the data expansion of a pedestrian image sample in a sample dataset; carrying out the grouping of the pedestrian image sample in the sample dataset after expansion, and obtaining a plurality of pedestrian sample groups; selecting samples, building a multi-channel convolution neural network, and extracting the global and local features of the sample data through the multi-channel convolution neural network; setting a loss function, calculating a loss value of the multi-channel convolution neural network, and optimizing the multi-channel convolution neural network; carrying out the feature classification of each global-local feature through the optimized multi-channel convolution neural network, and obtaining the feature class of each pedestrian sample group. The method enables the sample data to be expanded, meets the condition that triple loss exerts strict requirements for an input sample, can guarantee the robustness through employing multi-loss to optimize the multi-channel convolution neural network, and is suitable for the processing of pedestrian image features of a plurality of scenes.

Description

technical field [0001] The present invention mainly relates to the technical field of image processing, in particular to a pedestrian image feature classification method and system. 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 new computer application fields. Due to traditional methods such as K-NN, PCA, LDA, etc., the recognition rate of TOP1 is not high and is easily affected by the external environment, and its robustness is poor. However, most of the current pedestrian recognition methods using deep learning use a single loss function to optimize the network. When the picture scene changes greatly, it cannot guarantee robustness a...

AI Technical Summary

Problems solved by technology

Computer vision is an important part in new computer application fields. Due to traditional methods such as K-NN, PCA, LDA, etc., the recognition rate of TOP1 is not high and is easily affected by the external environment, and its robustness is poor.

However, most of the current pedestrian recognition methods using deep learning use a single loss function to optimize the network. When the picture scene changes greatly, the robustness cannot be guaranteed, and it cannot be applied to multiple scenes.

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