CN112906606B - Domain self-adaptive pedestrian re-identification method based on mutual divergence learning - Google Patents
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Abstract
The invention discloses a domain self-adaptive pedestrian re-identification method based on mutual divergence learning, which comprises the following steps: preparing a pedestrian data set; pre-training is carried out on a source domain data set, and feature vectors of pictures are extracted on a target domain data set; clustering the images of the target domain data set based on density, and taking the serial numbers of the clusters as pseudo tags; adding outliers to the training samples using a strategy of antagonism; mixing the clustered samples and outliers, sending the mixture into a network, correcting the noise of the pseudo tag by adopting mutual divergence learning, inputting the pedestrian image to be queried into a trained pedestrian re-recognition model to obtain a pedestrian feature vector to be recognized, and comparing and sequencing the pedestrian feature vector with attribute features in a candidate library to obtain a pedestrian re-recognition result. The invention reduces the distribution difference between the source domain and the target domain, effectively utilizes the knowledge of the source domain, and finally the framework of the invention can learn the characteristics with robustness and authentication.
Description
Technical Field
The invention discloses a method for realizing self-adaptive pedestrian re-identification in an unsupervised domain by applying deep learning and mutual learning, and belongs to the field of computer vision.
Background
Pedestrian re-identification (re-ID) is a technique that aims to establish identity correspondence between different cameras, determine images of different cameras, or whether a particular pedestrian is present in a video sequence, and is generally considered as a sub-problem of image retrieval. Great attention has been paid to, and significant progress has been made in the last decade. The pedestrian re-identification technology is widely applied to tracking the track of a person in a larger area, and has high application value in the fields of robotics, intelligent video monitoring, automatic photo labeling and the like.
At present, compared with the mature face recognition technology, pedestrian re-recognition is still a difficult problem in the field of computer vision. The main challenge is that the same target is affected by visual angle change, illumination change, posture change, pedestrian shielding, background noise interference and the like under different cameras, so that characteristic representation under different visual angles has a certain degree of deviation. The existing pedestrian re-recognition method is generally based on deep learning, and basically comprises the steps of calculating the similarity or distance between images, sorting samples according to the similarity or distance, and finally finding out images belonging to the same person as the pedestrian image to be queried. However, due to the limitation of resolution of the monitoring video, it is difficult to directly search for the same target through the face in the application of the actual monitoring field, however, searching for pedestrians by using the external features such as the wearing appearance of the pedestrians is an alternative method.
Most of the work currently in studying pedestrian re-identification is focused on supervised learning methods, which rely heavily on the acquisition of large-scale data sets and accurate manual labeling, which is often a time-consuming and cumbersome task. While the re-ID method achieves very good results under the supervision of such large-scale data sets, they often suffer from catastrophic performance degradation when we apply the trained reid model directly to new camera systems due to the existence of domain differences. Thus, current research emphasis has been shifted to Unsupervised Domain Adaptation (UDA), which attempts to shift a model trained on a labeled source domain dataset to an unlabeled target domain dataset. Despite the remarkable progress made by the cluster-based UDA method, unavoidable pseudo tag noise due to limited transmissibility of source domain features, imperceptibility of target domain image tags, and imperfection of clustering results remains an obstacle factor for performance improvement.
Reference is made to:
[1].LIN,Yutian,et al.A bottom-up clustering approach to unsupervised person re-identification[C].Proceedings of the AAAI Conference on Artificial Intelligence,2019.p.8738-8745.
[2] wang, zhongdao, zheng, liang, li, yali, et al Linkage Based Face Clustering via Graph Convolution Network [ J ].
[3].Yixiao Ge,Dapeng Chen,and Hongsheng Li,“Mutual mean-teaching:Pseudo label refinery for unsupervised domain adaptation on person re-identification,”in International Conference on Learning Representations,2020.
[4].Yunpeng Zhai,Shijian Lu,Qixiang Ye,Xuebo Shan,Jie Chen,Rongrong Ji,and Yonghong Tian,“Ad-cluster:Augmented discriminative clustering for domain adaptive person re-identification,”in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2020,pp.9021–9030.
Disclosure of Invention
In order to solve the defects of the scheme, the invention aims to provide a domain self-adaptive pedestrian re-identification method based on mutual divergence learning so as to reduce the distribution difference between a source domain and a target domain.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a domain self-adaptive pedestrian re-identification method based on mutual divergence learning comprises the following steps:
step S1, preparing a pedestrian data set, wherein the pedestrian data set comprises a labeled source domain data set and an unlabeled target domain data set;
step S2, pre-training is carried out on a source domain data set, and feature vectors of pictures are extracted on a target domain data set;
step S3, clustering the images of the target domain data set based on density, and taking the serial numbers of the clusters as pseudo tags;
step S4, adding outliers into the training samples by using a strategy of antagonism;
and S5, mixing the clustered samples obtained in the step S3 and the outliers obtained in the step S4, sending the mixture into a network, correcting the noise of the pseudo tag by adopting mutual divergence learning, inputting the pedestrian image to be queried into a trained pedestrian re-recognition model to obtain a pedestrian feature vector to be recognized, and comparing and sequencing the feature vector with the attribute features in the candidate library to obtain a pedestrian re-recognition result.
In the step S2, a ResNet-50 model is utilized to conduct supervised pre-training on a source domain data set, then a pre-training model is utilized to initialize a training target domain data set, and characteristics of pictures in the target domain data set are extracted.
In the step S2, initializing by using a pre-trained model, and removing the last classification layer of the ResNet-50 model; extracting feature vectors of pictures on a target domain dataset is denoted as x= { X 1 ,x 2 ,...,x N N is the number of sample pictures in the dataset, each x is a 2048-dimensional feature vector.
In the step S2, the pre-trained model is optimized for cross entropy loss and triplet loss by using a loss function.
The step S3 includes:
step S31, calculating the distance between each image and all other images in the target domain data set;
step S32, grouping samples in a high-density region into clusters and keeping samples in a low-density region as outliers using a density-based clustering algorithm (DBSCAN) for the distance between each image;
step S33, for the images that have been clustered into clusters, using the numbers of the clusters in which they are located as their pseudo tags, and then performing optimization training on the pseudo tags.
In the step S4, an additional auxiliary network is used, and a part of clustered images are sent into the auxiliary network for learning, so that the auxiliary network can obtain the general characteristics of the clustered samples; the outliers are then fed into the auxiliary network, which can extract some outliers with small losses.
In the step S5, the training and the optimization of the pseudo tag are alternately performed by correcting the pseudo tag through the mutual divergence learning: in the early stage of training, the difference of two networks is maintained by inputting the same images but respectively carrying out random erasing, clipping and overturning and carrying out different parameter initialization; in the middle and later stages of training, adopting a divergence strategy to slow down the speed of two networks to reach consensus, and keeping continuous divergence between the two networks; executing each network to perform own prediction, and selecting samples with prediction differences between the two networks; based on these samples, each network further trains the divergent samples and updates their parameters.
In said step S5, a momentum-based moving average model is proposed for each peer-to-peer network.
Compared with the prior art, the technical scheme has the following beneficial effects:
(1) The invention provides an unsupervised domain self-adaptive pedestrian re-identification method based on mutual divergence learning, which reduces the distribution difference between a source domain and a target domain, effectively utilizes the knowledge of the source domain, and finally the framework of the invention can learn the characteristics with robustness and discrimination.
(2) The invention adopts the antagonism strategy to gradually add the outlier in the clustering result to the training process, thereby increasing the diversity and reliability of the training samples.
(3) Experimental results on three large datasets demonstrated the superiority of the framework presented in this invention over other up-to-date methods.
Drawings
Fig. 1 is a flow chart of domain adaptive pedestrian re-recognition based on mutual divergence learning.
Detailed Description
The present invention will be further explained below.
As shown in fig. 1, the domain adaptive pedestrian re-recognition method based on mutual divergence learning of the invention comprises the following steps:
step 1, preparation and pretreatment of a data set:
the dataset includes a source domain dataset with complete annotation information and a target domain dataset without any manual annotation information.
Three public data sets Market-1501, dukeMTMC-ReID and MSMT17 commonly used in the field of pedestrian re-recognition research are used as the data sets of the training model. Market-1501 this dataset contains 1501 pedestrians and 32688 tagged images from 6 different cameras. Of all images, 12936 images of 751 pedestrians were used for training, 3368 images of another 750 pedestrians were used for query, and 19732 images of 750 pedestrians were used as a library of bellery charts. The identities between the training image and the gallery image are disjoint. The DukeMTMC-ReID dataset consisted of video captured outdoors from 8 cameras containing 1404 pedestrians and 36411 tagged images, with a training set of 16522 images of 702 pedestrians, 17661 images for gallery and 2228 images for query. The MSMT17 dataset is the most challenging dataset taken by 15 cameras, containing 126441 images of 4101 pedestrians.
Step 2: the model ResNet-50 with the best feature extraction effect in the pedestrian re-recognition field is utilized to pretrain on the source domain data set and extract the features of the target domain sample. A supervised pre-training is performed on the source domain dataset with complete tag information, and then the training target domain dataset is initialized with a pre-trained model. Specifically, supervised training is performed on the source domain dataset Market1501, and the network is optimized using cross entropy loss and triplet loss, resulting in a pre-trained model. Then using the model as an initial model for target domain dataset learning, and removing the ResNet-50 last classification layer; sending the unlabeled source domain data set into a pre-trained model, and extracting the feature vector of each picture; the sample characteristics are expressed as x= { X 1 ,x 2 ,...,x N N is the number of sample pictures in the dataset, each x is a 2048-dimensional feature vector.
Step 3, for the feature vector of each image in step 2, calculating the distance between the images, then using a density-based clustering algorithm (DBSCAN) for the distance, grouping samples in a high density area into clusters, and keeping samples in a low density area as outliers, so that the target domain dataset can be divided into clustered points and outliers. For points that have been clustered, the number of the cluster in which they are located is used as their pseudo tag. For outliers, a strategy of resistance is used in step 4 to add small loss samples to the training process.
Step 4, for the generated outliers of step 3, an attempt is made to gradually append some of the small-loss samples in the outliers to the training process using an auxiliary network, which not only effectively uses these difficult samples, but also further improves the performance of the model. The primary model and secondary network are trained alternately so that small loss samples of relatively high confidence can be added smoothly to the training process. This results in a more reliable, more diverse training sample containing clustered samples and less missing outliers. However, the supervision information may be noisy due to imperfections in the clustering results and the addition of outliers.
In order to prevent the re-ID model from being affected by noisy labels, a mutual divergence learning is proposed to correct the pseudo labels, and training and optimization of the pseudo labels are alternated. In the early stage of training, the difference of two networks is maintained by inputting the same image but respectively performing random erasing, clipping and flipping, performing different parameter initialization and the like. In the middle and later stages of training, a bifurcation strategy is attempted to slow down the speed at which the two networks reach consensus and maintain a persistent bifurcation between the two networks. Specifically, each network is executed to make its own prediction, and a sample in which there is a prediction difference between two networks is selected. Based on these hard samples, which are in fact difficult but valuable, each network further trains these divergent samples and updates their parameters. In order to prevent the two networks from converging close to each other, a momentum-based moving average model is proposed for each peer-to-peer network.
And finally, inputting the pedestrian image to be queried into a trained pedestrian re-recognition model to obtain a pedestrian feature vector to be recognized, and comparing and sequencing the pedestrian feature vector with the attribute features in the candidate library to obtain a pedestrian re-recognition result.
In summary, according to the domain adaptive pedestrian re-recognition method based on mutual divergence learning, clustering is performed on an unlabeled dataset by using DBSCAN, and then outliers with small loss are added into a training process by using a strategy of antagonism, so that the pedestrian characteristics with identification are learned. The method reduces the complexity of calculation, saves a great amount of manual labeling cost, and effectively improves the accuracy of pedestrian matching retrieval.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (6)
1. A domain self-adaptive pedestrian re-identification method based on mutual divergence learning is characterized by comprising the following steps of: the method comprises the following steps:
step S1, preparing a pedestrian data set, wherein the pedestrian data set comprises a labeled source domain data set and an unlabeled target domain data set;
step S2, pre-training is carried out on a source domain data set, and feature vectors of pictures are extracted on a target domain data set;
step S3, clustering the images of the target domain data set based on density, and taking the serial numbers of the clusters as pseudo tags;
step S4, adding outliers into the training samples by using a strategy of antagonism;
in the step S4, an additional auxiliary network is used, and a part of clustered images are sent into the auxiliary network for learning, so that the auxiliary network can obtain the general characteristics of the clustered samples; then the outliers are sent into an auxiliary network, so that some outliers with small loss can be extracted;
step S5, mixing the clustered samples obtained in the step S3 and the outliers obtained in the step S4, sending the mixture into a network, correcting the noise of the pseudo tag by adopting mutual divergence learning, inputting the pedestrian image to be queried into a trained pedestrian re-recognition model to obtain a pedestrian feature vector to be recognized, and comparing and sequencing the pedestrian feature vector with the attribute features in the candidate library to obtain a pedestrian re-recognition result;
in the step S5, the training and the optimization of the pseudo tag are alternately performed by correcting the pseudo tag through the mutual divergence learning: in the early stage of training, the difference of two networks is maintained by inputting the same images but respectively carrying out random erasing, clipping and overturning and carrying out different parameter initialization; in the middle and later stages of training, adopting a divergence strategy to slow down the speed of two networks to reach consensus, and keeping continuous divergence between the two networks; executing each network to perform own prediction, and selecting samples with prediction differences between the two networks; based on these samples, each network further trains the divergent samples and updates their parameters.
2. The domain adaptive pedestrian re-recognition method based on the mutual divergence learning as claimed in claim 1, wherein: in the step S2, a ResNet-50 model is utilized to conduct supervised pre-training on a source domain data set, then a pre-training model is utilized to initialize a training target domain data set, and characteristics of pictures in the target domain data set are extracted.
3. The domain adaptive pedestrian re-recognition method based on the mutual divergence learning as claimed in claim 2, wherein: in the step S2, initializing by using a pre-trained model, and removing the last classification layer of the ResNet-50 model; extracting feature vectors of pictures on a target domain dataset is denoted as x= { X 1 ,x 2 ,...,x N N is the number of sample pictures in the dataset, each X is a 2048-dimensional feature vector.
4. The domain adaptive pedestrian re-recognition method based on the mutual divergence learning as claimed in claim 2, wherein: in the step S2, the pre-trained model is optimized for cross entropy loss and triplet loss by using a loss function.
5. The domain adaptive pedestrian re-recognition method based on the mutual divergence learning as claimed in claim 1, wherein: the step S3 includes:
step S31, calculating the distance between each image and all other images in the target domain data set;
step S32, grouping samples in a high-density area into clusters and keeping samples in a low-density area as outliers by using a density-based clustering algorithm for the distance between each image;
step S33, for the images that have been clustered into clusters, using the numbers of the clusters in which they are located as their pseudo tags, and then performing optimization training on the pseudo tags.
6. The domain adaptive pedestrian re-recognition method based on the mutual divergence learning as claimed in claim 1, wherein: in said step S5, a momentum-based moving average model is proposed for each peer-to-peer network.
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