Pedestrian re-identification model, method and system for adaptive difficulty mining

Abstract

The invention discloses a pedestrian re-identification model, method and system for adaptive difficulty mining. The identification method comprises the steps of: randomly dividing sample pictures intoa training set used for each iteration, inputting the training set into a convolutional neural network, obtaining the probability that each sample pair belongs to a positive or negative sample pair by using a softmax function, and then obtaining the loss of each sample pair by using a multinomial logistic function; obtaining a difficult sample pair by using the loss of each sample pair; and training the convolutional neural network by using the difficult sample pair until the current number of iterations reaches the upper limit of the number of iterations, thus obtaining the pedestrian re-identification model. The pedestrian re-identification model is used to extract features of each picture in a picture set to be identified, and then a similarity order of the sample pairs in the pictureset to be identified is obtained. The pedestrian re-identification model, method and system avoid over-fitting and under-fitting, and have high recognition accuracy.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and more specifically relates to a pedestrian re-identification model, method and system for adaptive difficult example mining. Background technique [0002] Pedestrian re-identification algorithm is one of the important fields of image processing and pattern recognition research. The so-called re-identification of pedestrians refers to an algorithm that uses a pending searched target pedestrian as the search source, and automatically finds the same target in videos captured by other cameras. The influence of interfering factors such as , occlusion, etc. shows great intra-class differences. With the development of deep learning technology, the accuracy of person re-identification algorithm has made great progress compared with traditional methods such as manual feature and metric learning. However, the number of samples required for deep learning training is huge, and for the task o...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a pedestrian re-identification model, method and system for self-adaptive difficult example mining, thereby solving the problem of over-fitting, under-fitting, and recognition accuracy in the prior art. low technical issues

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