A pedestrian re-identification method based on uncertainty optimization
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An uncertainty and identification method technology, applied in the field of pedestrian re-identification based on uncertainty optimization, can solve the problems of pedestrian over-fitting and uncertainty, low recognition rate, etc., and improve accuracy , the effect of improving accuracy and robustness
Active Publication Date: 2019-06-14
TONGJI UNIV
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However, this method does not take into account the overfitting and uncertainty problems in the process of pedestrian re-identification. The samples, parameters, and structural uncertainties in deep learning will have a greater negative impact on the results of pedestrian re-identification, especially when pedestrians are re-identified. When the number of samples is small, the recognition rate is low
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[0051] In order to make the object, technical scheme and advantages of the present invention clearer, below in conjunction with embodiment, specifically as figure 1 The shown algorithm flow chart further describes the present invention in detail. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.
[0052] Step 1: Input processing, specifically described as follows: The present invention uses a twin network structure, and uses two original images belonging to the same or different pedestrians as the input of two isomorphic networks, and the specific implementation is as follows:
[0053] (1) The present invention arbitrarily takes two images from the pedestrian image, divides them into multiple image pairs, and uses them as the input of the Siamese network;
[0054] (2) The present invention cuts and normalizes the processed picture pairs, and simultaneously performs opera...
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[0095] figure 1 It is the realization flowchart of the present invention, and the specific implementation mode is as follows:
[0096] 1. Twin network input processing;
[0097] 2. Construct a Bayesian convolutional neural network;
[0098] 3. Calculate the multi-classification loss function during training;
[0099] 4. Calculate the binary classification loss function during training;
[0100] 5. Weighted multi-classification and binary classification loss functions during training, backpropagation optimizes Bayesian convolutional neural network parameters;
[0101] 6. Use the Euclidean distance calculation formula during the test to obtain the final distance between the pedestrian image to be identified and the comparison image;
[0102] 7. Sort the distances during the test, and obtain the matching and sorting of the comparison image library corresponding to the pedestrian to be identified.
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Abstract
The invention relates to the field of computer vision, adopts a deep learning framework, and particularly relates to a pedestrian re-identification method based on uncertainty optimization, which comprises the following steps of 1) using a twin network structure to respectively use two original images belonging to the same or different pedestrians as the input of two isomorphic networks; 2) usingan inception network and a Dropout layer superposition mode to realize the Bayesian convolutional neural network with uncertainty optimization as a feature extraction network; 3) calculating binary classification loss and multi-classification loss of respective networks according to feature output of the twin network, and superposing the binary classification loss and the multi-classification lossfor back propagation and parameter optimization of the networks; 4) inputting the pedestrian image to be identified and all comparison images into the trained model, and extracting image features; 5)obtaining a final distance between the pedestrian image to be identified and the comparison image by using a Euclidean distance calculation formula; and 6) performing sorting according to the featuresimilarity distance to obtain a comparison image matching sorting corresponding to the to-be-identified pedestrian. Compared with the prior art, the method has the advantages of high accuracy, high robustness, rapidness, simplicity, convenience and the like under the conditions of all samples and few samples.
Description
technical field [0001] The present invention relates to the field of computer vision and adopts a deep learning framework. More specifically, the present invention relates to a pedestrian re-identification method based on uncertainty optimization. Background technique [0002] Pedestrian re-identification is a key part of intelligent video analysis to break through the bottleneck of mass surveillance video technology application, which has attracted the attention of many researchers in recent years. Pedestrian re-identification refers to matching the same pedestrian target at different times and under different cameras. Pedestrian re-identification is a very challenging research problem. In the large-scale video surveillance network system in real life, the complexity and unpredictability of the environment and equipment will bring various uncertainties to the realization of pedestrian re-identification. First, due to different hardware conditions and parameters of differen...
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