CN110110689B - A Pedestrian Re-identification Method - Google Patents

Abstract

The embodiment of the disclosure relates to a pedestrian re-identification method, which comprises the following steps: extracting a pedestrian CNN characteristic diagram from a plurality of pictures; simulating the situation that the discriminant area of the CNN feature map of the pedestrian is blocked by adopting an anti-erasure learning mode to perform model training to obtain a training model; and carrying out pedestrian re-recognition by combining the training model with the target pedestrian image and the pedestrian image to be recognized to obtain a pedestrian re-recognition result. The method provided by the embodiment of the disclosure provides a feature level data enhancement strategy, the input feature map of the auxiliary classifier is partially erased, the variation of pedestrian features is increased, the situation that pedestrians are blocked is resisted, and the generalization capability of a deep pedestrian re-recognition model is improved.

Description

A Pedestrian Re-identification Method

technical field

The present disclosure relates to the technical field of computer vision, in particular to a pedestrian re-identification method.

Background technique

Pedestrian re-identification is to match and identify pedestrian identities under the non-overlapping multi-camera surveillance system, and it plays an important role in intelligent video surveillance, crime prevention, and maintenance of social order. However, when human body attributes such as posture, gait, clothing, and environmental factors such as illumination and background change, the appearance of the same pedestrian will be significantly different under different surveillance videos, while the appearance of different pedestrians will be relatively similar under certain circumstances.

In recent years, deep learning methods have been widely used. Compared with traditional manual design methods, deep learning can achieve better performance. However, deep person re-identification models usually have a large number of network parameters, but are optimized on a limited data set, which increases the risk of over-fitting and reduces the generalization ability. Therefore, improving the generalization ability of the model is a meaningful and important issue for deep person re-identification.

In order to improve the generalization ability of the deep convolutional neural network, it is possible to increase the variants of the training data set and collect a large number of pedestrian images containing occlusions, but only image-level data enhancement can be achieved, and aspects other than the image level cannot be provided. Perform data augmentation to improve the generalization ability of deep convolutional neural networks.

The above-mentioned disadvantages are expected to be overcome by those skilled in the art.

Contents of the invention

(1) Technical problems to be solved

In order to solve the above-mentioned problems in the prior art, the present disclosure provides a pedestrian re-identification method, which can perform data enhancement at the feature level to improve the generalization ability of a deep convolutional neural network.

(2) Technical solution

In order to achieve the above object, the main technical solutions adopted in this disclosure include:

An embodiment of the present disclosure provides a pedestrian re-identification method, which includes:

Extract pedestrian CNN feature maps from multiple pictures;

Adopting the mode of anti-erasing learning to imitate the situation that the discriminative region of the pedestrian CNN feature map is blocked is carried out model training to obtain the training model;

The pedestrian re-identification is performed by using the training model combined with the target pedestrian image and the pedestrian image to be recognized, and the pedestrian re-identification result is obtained.

In one embodiment of the present disclosure, the extraction of pedestrian CNN feature maps from multiple pictures includes:

randomly selecting the plurality of pictures from the training data set;

The plurality of pictures are input to a plurality of different semantic layers of the ResNet50 model to extract, and obtain feature maps of a plurality of channels;

Using a channel attention module to process the feature maps of the plurality of channels to obtain channel-processed feature maps;

A spatial attention module is used to process the spatial context information of the channel-processed feature map at different positions to obtain the pedestrian CNN feature map.

In an embodiment of the present disclosure, the process of using the channel attention module to process the feature maps of the multiple channels, and obtaining the channel-processed feature maps includes:

Obtaining a channel feature descriptor according to the feature map of each channel in the feature map of the plurality of channels;

Obtaining a channel attention feature map through an activation function operation on the channel feature descriptor;

The channel-attention feature map is multiplied by the feature map aggregated by the feature map to obtain the channel-processed feature map.

In an embodiment of the present disclosure, the feature descriptor includes statistical values of the multiple channels, and the feature descriptor is:

The statistical value of each channel is:

Where N is the number of channels, n is the number of channels, and A and B are the length and width of the feature map, respectively;

The channel attention feature map is:

e=σ(W ₂ δ(W ₁ (s)))

是第一全连接层Fc1的权重，/>

是第二全连接层Fc2的权重，r是衰减的倍数。Where σ and δ represent the Sigmod activation function and the ReLU activation function respectively,

is the weight of the first fully connected layer Fc1, />

is the weight of the second fully connected layer Fc2, and r is the multiple of attenuation.

In an embodiment of the present disclosure, the spatial context information of the channel-processed feature map at different positions is processed by using the spatial attention module, and obtaining the pedestrian CNN feature map includes:

performing a 1×1 convolution operation on the channel-processed feature map to obtain a first spatial information feature map T and a second spatial information feature map U;

Perform matrix multiplication with the transposition of the first spatial information feature map T and the second spatial information feature map U to obtain a spatial attention feature map;

performing a 1×1 convolution operation on the channel-processed feature map to obtain a third spatial information feature map V;

Perform matrix multiplication operation with the transposition of the third spatial information feature map V and the spatial attention feature map to obtain a spatially processed feature map;

Obtaining the pedestrian CNN feature map according to the channel processing and the spatial processing.

In one embodiment of the present disclosure, the model training is carried out by imitating the case where the discriminative area of the pedestrian CNN feature map is blocked by means of anti-erasing learning, and the obtained training model includes:

The pedestrian CNN feature map is input to the main classifier and the auxiliary classifier respectively for classification training, and the pedestrian category-specific feature map is output from the main classifier and the auxiliary classifier;

Perform partial erasure in the auxiliary classifier to obtain a feature map after erasure;

The pedestrian category-specific feature map output by the main classifier and the erased feature map output by the auxiliary classifier are respectively calculated through a loss function to obtain a loss value;

Perform parameter update on the training model according to the loss value.

In one embodiment of the present disclosure, the main classifier and the auxiliary classifier include the same number of convolutional layers and global average pooling layers, and the number of channels of the convolutional layer is the same as the number of pedestrians in the training data set. The number of categories is the same, and each channel of the pedestrian category-specific feature map represents the body response heat map when the pedestrian image belongs to different categories.

In an embodiment of the present disclosure, performing partial erasing on the auxiliary classifier includes:

Determining the area where the heat map value in the body response heat map is higher than the set resistance erasure threshold as a discriminative area;

The portion corresponding to the discriminative region in the pedestrian category-specific feature map output by the auxiliary classifier is erased in an adversarial manner by replacing the response value with 0.

In an embodiment of the present disclosure, using the training model to perform pedestrian re-identification in combination with the target pedestrian image and the pedestrian image to be recognized, and obtaining the result of pedestrian re-identification includes:

Inputting the image of the target pedestrian and the image of the pedestrian to be identified into the training model for training to obtain corresponding depth features;

calculating a cosine distance according to the depth feature of the target pedestrian image and the depth feature of the pedestrian image to be identified;

Determine the similarity between the target pedestrian image and the pedestrian image to be recognized according to the size of the cosine distance, wherein the pedestrian image to be recognized with the largest similarity is the pedestrian re-identification result.

In one embodiment of the present disclosure, the calculation formula for calculating the cosine distance according to the depth features of the target pedestrian image and the depth feature of the pedestrian image to be recognized is:

Where feat1 is the depth feature of the target pedestrian image, and feat2 is the depth feature of the pedestrian image to be recognized.

(3) Beneficial effects

The beneficial effects of the present disclosure are: the pedestrian re-identification method provided by the embodiment of the present disclosure, by providing a feature-level data enhancement strategy, the input feature map of the auxiliary classifier is partially erased, and the variation of pedestrian features and the resistance to pedestrians are increased. In the case of occlusion, the generalization ability of the deep person re-identification model is improved.

Description of drawings

FIG. 1 is a flowchart of a pedestrian re-identification method provided by an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a network structure for implementing the method described in FIG. 1 in an embodiment of the present disclosure;

FIG. 3 is a flowchart of step S110 in FIG. 1 according to an embodiment of the present disclosure;

FIG. 4 is a flowchart of step S303 in FIG. 3 according to an embodiment of the present disclosure;

FIG. 5 is a schematic diagram of channel attention in an embodiment of the present disclosure;

FIG. 6 is a schematic diagram of spatial attention in an embodiment of the present disclosure;

FIG. 7 is a flowchart of step S304 in FIG. 3 according to an embodiment of the present disclosure;

FIG. 8 is a schematic diagram of anti-erasure learning in an embodiment of the present disclosure;

FIG. 9 is a flowchart of step S120 in FIG. 1 according to an embodiment of the present disclosure;

FIG. 10 is a flowchart of step S130 in FIG. 1 according to an embodiment of the present disclosure.

Detailed ways

In order to better explain the present disclosure and facilitate understanding, the present disclosure will be described in detail below through specific implementation manners in conjunction with the accompanying drawings.

All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terms used herein in the description of the present disclosure are for the purpose of describing specific embodiments only, and are not intended to limit the present disclosure. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

In other embodiments of the present disclosure, increasing the variants of the training data set is an effective way to improve the generalization ability of the deep convolutional neural network. However, unlike the visual task of object recognition, person re-identification needs to collect image data across cameras, and pedestrian labeling is also very difficult. Building a large enough dataset for person re-identification often requires high cost investment, resulting in existing datasets. Pedestrian labels are small. To solve this problem, data augmentation can only use the current data set to increase the variant of the training set samples without other costs. Recent research on data enhancement uses Generative Adversarial Networks (GAN) to generate pedestrian images with different human poses and camera styles, but this method has problems such as long training time, difficult convergence, and low quality of generated images. In addition to explicitly generating new images, the usual approach is to augment the data by dithering pixel values, randomly cropping, flipping the original image, etc. on the training image.

In addition, occlusion is also an important factor affecting the generalization ability of convolutional neural networks. Collecting a large number of pedestrian images including occlusion situations is an effective way to solve the occlusion problem, but it also requires high cost input. Another more reasonable method is to accurately simulate the situation where pedestrians are occluded. For example, use a rectangular frame of random size and random position on the training image to occlude the training image, and replace the pixel values of this rectangular area with random values to imitate the occlusion to increase the variation of the dataset. However, the above occluded areas are randomly selected. Optionally, train a pedestrian re-identification classification model, and then find the discriminative area of the image with the assistance of network visualization and multiple classifiers, and block the discriminative area on the original image. Generate new samples, and finally add the new samples to the original data set to retrain the pedestrian re-identification model.

Based on the above two methods, both are variants of adding samples through occlusion on the original pedestrian image, which belong to image-level data enhancement methods, and this application provides one.

Fig. 1 is a flowchart of a pedestrian re-identification method provided by an embodiment of the present disclosure. As shown in Fig. 1, the method includes the following steps:

As shown in Figure 1, in step S110, extract the pedestrian CNN feature map from multiple pictures;

As shown in Figure 1, in step S120, adopt the mode of anti-erasure learning to imitate the situation that the discriminative region of described pedestrian CNN feature map is blocked and carry out model training, obtain training model;

As shown in FIG. 1 , in step S130 , the training model is used to perform pedestrian re-identification in combination with the target pedestrian image and the image of the pedestrian to be recognized, to obtain a pedestrian re-identification result.

The specific implementation of each step of the embodiment shown in Figure 1 is described in detail below:

Combined with the process shown in FIG. 1, FIG. 2 is a schematic diagram of the network structure for implementing the method described in FIG. 1 in an embodiment of the present disclosure. As shown in FIG. 2, channel attention and spatial attention are required for each channel during processing Complementary attention of force, and then perform adversarial erasure learning and Softmax loss calculation on the resulting feature maps. In addition, as shown in Figure 2, the three channels are divided into two channels as an intermediate semantic branch and a high-level semantic branch.

In step S110, pedestrian CNN feature maps are extracted from multiple pictures.

FIG. 3 is a flowchart of step S110 in FIG. 1 according to an embodiment of the present disclosure, which specifically includes the following steps:

As shown in FIG. 3, in step S301, the multiple pictures are randomly selected from the training data set.

As shown in FIG. 3, in step S302, the multiple pictures are input to multiple different semantic layers of the ResNet50 model for extraction, and feature maps of multiple channels are obtained.

In an embodiment of the present disclosure, in this step, firstly, an image is input, and a batch number of picture sets are randomly selected from the training data set, that is, multiple pictures are selected. Secondly, adjust the size of the picture to 384*128, and send it to the different semantic layers of the backbone network ResNet50 (res_conv5a, res_conv5b, res_conv5c, as shown in Figure 2, where res_conv5a, res_conv5b correspond to the intermediate semantic branch, and res_conv5c corresponds to the advanced semantic branch ) to extract pedestrian CNN feature maps.

As shown in FIG. 3 , in step S303 , the channel attention module is used to process the feature maps of the multiple channels to obtain channel-processed feature maps.

FIG. 4 is a flowchart of step S303 in FIG. 3 according to an embodiment of the present disclosure, which specifically includes the following steps:

As shown in FIG. 4 , in step S401 , a channel feature descriptor is obtained according to the feature map of each channel in the feature maps of the plurality of channels.

As shown in FIG. 4 , in step S402 , the channel feature descriptor is operated with an activation function to obtain a channel attention feature map.

As shown in FIG. 4 , in step S403 , the channel-attention feature map is multiplied by the feature map aggregated from the feature map to obtain the channel-processed feature map.

In one embodiment of the present disclosure, step S303 can use the channel attention module to explore the connection between the channels of the pedestrian CNN feature map, capture and describe the discriminative regions of the input image.

FIG. 5 is a schematic diagram of channel attention in an embodiment of the present disclosure. As shown in FIG. 5 , for the feature map extracted for each channel, A and B are the length and width of the feature map, and n is the number of the channel. N is the number of channels.

每一个通道的空间信息，产生通道的特征描述子：/>

可见，所述特征描述子包括所述多个通道的统计值，每一通道的统计值为：First, the feature maps are aggregated using the GAP operation

The spatial information of each channel generates the feature descriptor of the channel: />

It can be seen that the feature descriptor includes statistical values of the multiple channels, and the statistical value of each channel is:

Second, pass s through a threshold mechanism module to obtain the channel attention feature map

e=σ(W ₂ δ(W ₁ (s))) formula (2)

是第一全连接层Fc1的权重，/>

是第二全连接层Fc2的权重，r是衰减的倍数。Where σ and δ represent the Sigmod activation function and the ReLU activation function respectively,

is the weight of the first fully connected layer Fc1, />

is the weight of the second fully connected layer Fc2, and r is the multiple of attenuation.

Finally, the channel attention e is multiplied by the original input feature map S to obtain the corrected feature map

Since the channel attention feature map e encodes the dependencies and relative importance between channel feature maps, the neural network will dynamically update e to learn important types of feature maps and ignore feature maps that are less reused.

As shown in FIG. 3 , in step S304 , the spatial context information at different positions of the channel-processed feature map is processed by using a spatial attention module to obtain the pedestrian CNN feature map.

In one embodiment of the present disclosure, step S304 may use a spatial attention module to integrate spatial context information at different positions of the feature map into the local features of pedestrians, so as to enhance the spatial correlation of the local areas of pedestrians. FIG. 6 is a schematic diagram of spatial attention in an embodiment of the present disclosure. As shown in FIG. 6 , convolution operations are performed on the channel-processed feature maps to obtain the first spatial information feature map T, the second spatial information feature map U and The third spatial information feature map V and T are transposed and multiplied by U to obtain D, and D and V are multiplied to obtain X. X is scaled to a certain ratio and added to the channel-processed feature map to realize feature The spatial processing of the graph yields the final pedestrian CNN feature map.

FIG. 7 is a flowchart of step S304 in FIG. 3 according to an embodiment of the present disclosure, which specifically includes the following steps:

送进1×1的卷积f_key与f_query，得到两个特征图T和U，其中

As shown in FIG. 7 , in step S701 , a 1×1 convolution operation is performed on the channel-processed feature map to obtain a first spatial information feature map T and a second spatial information feature map U. Channel attention corrected feature map (i.e. channel processed feature map)

Send 1×1 convolution f _key and f _query to get two feature maps T and U, where

其中Z＝A×B，代表特征的数量，之后将T进行转置并且与U进行矩阵乘法，按照行方向应用一个Softmax函数得到空间注意力特征图D∈R^Z×Z，D的每一个元素d_j,i可以表示为：As shown in FIG. 7 , in step S702 , matrix multiplication is performed on the transposition of the first spatial information feature map T and the second spatial information feature map U to obtain a spatial attention feature map. Adjust the T and U shapes to

Where Z=A×B represents the number of features, then transpose T and perform matrix multiplication with U, and apply a Softmax function in the row direction to obtain the spatial attention feature map D∈R ^Z×Z , each element of D d _j,i can be expressed as:

Among them, d _j,i represents the correlation of the i-th position to the feature of the j-th position, the more similar the feature expressions of the two positions are, the higher the correlation between them is.

As shown in FIG. 7 , in step S703 , a 1×1 convolution operation is performed on the channel-processed feature map to obtain a third spatial information feature map V.

并将其形状调整为/>

Send the channel-processed feature map S' into the 1×1 convolutional layer f _value to get a new feature map

and reshape it to />

As shown in FIG. 7 , in step S704 , matrix multiplication is performed on the transposition of the third spatial information feature map V and the spatial attention feature map to obtain a spatially processed feature map.

并将其通过1×1的卷积f_up，得到特征图/>

In this step, matrix multiplication is first performed on the transpose of V and D, and the shape of the result is adjusted as

And pass it through the 1×1 convolution f _up to get the feature map />

As shown in Fig. 7, in step S705, the pedestrian CNN feature map is obtained according to the channel processing and the space processing, where the channel processing refers to the above steps S401-S403, and the space processing refers to the above-mentioned steps S701-S704.

即：In this step, X is multiplied by the scaling parameter α, and is added element-wise to the channel-processed feature map S' to obtain the feature map

Right now:

Based on the above, the elements of each position of the feature map S" can be expressed as:

Among them, α is a learnable parameter, which is initially set to 0, and can gradually learn a larger weight from 0. It can be seen from formula (6) that the feature S” _j of each position of the feature map S” is the weighted sum of the features of all positions and the channel-processed feature map S’ _j , so it contains the global receptive field, which can be calculated according to Spatial attention is paid to the size of each element dj _,i in the feature map D, selectively aggregating the spatial context information of related local regions V _i , so that the connection between different local features of pedestrians can be enhanced.

先通过通道注意力模块，再通过空间注意力模块，即实现互补注意力的修正：Based on the previous steps, it is more effective to use the modified CNN feature map in series with channel attention and spatial attention, so that the neural network can automatically focus on which types of features and which features. Therefore, in the present disclosure, the two modules of the channel attention module and the spatial attention module are jointly used to give full play to the roles of both. As shown in Figure 5, the feature map of the present disclosure

First through the channel attention module, and then through the spatial attention module, the correction of complementary attention is realized:

S'＝M _c (S)

S”＝M _s (S’) formula (7)

In step S120 , model training is performed by imitating the case where the discriminative region of the pedestrian CNN feature map is blocked by means of anti-erasure learning, to obtain a training model.

FIG. 8 is a schematic diagram of anti-erasure learning in an embodiment of the present disclosure. As shown in FIG. 8 , the CNN feature map of pedestrians is processed by convolution, GAP and Softmax loss functions through the main classifier and auxiliary classifier respectively.

FIG. 9 is a flowchart of step S120 in FIG. 1 according to an embodiment of the present disclosure, which specifically includes the following steps:

As shown in Figure 9, in step S901, the pedestrian CNN feature map is respectively input to the main classifier and auxiliary classifier for classification training, and the pedestrian category-specific features are output from the main classifier and the auxiliary classifier picture.

Wherein the main classifier and the auxiliary classifier include the same number of convolutional layers and global average pooling layers (Global Average Pooling, GAP for short), and the number of channels of the convolutional layer is the same as that in the training data set The number of pedestrian categories is the same, and each channel of the pedestrian category-specific feature map represents the body response heat map when the pedestrian images belong to different categories.

In this step, the fully connected layer of the classification model is replaced with a 1×1 convolutional layer to form a classification model based on a fully convolutional network, and the corrected feature map (i.e. pedestrian CNN feature map) is sent to the 1×1 convolutional layer. The convolutional layer can directly obtain the feature map specific to the pedestrian category. Since the number of channels in the convolutional layer is the number of pedestrian categories in the training set, each channel of the feature map represents the body response heat map when the pedestrian image belongs to a different category. In the training phase, the category label of the pedestrian image can be obtained, and the feature map of the channel corresponding to the category label can be indexed to obtain the specific feature map of the pedestrian category, that is, the body response heat map of the pedestrian image.

As shown in FIG. 9 , in step S902 , partial erasure is performed in the auxiliary classifier to obtain the feature map after erasure.

Firstly, the area whose heat map value in the body response heat map is higher than the set adversarial erasure threshold is determined as the discriminative area; secondly, the pedestrian category-specific feature map output by the auxiliary classifier corresponds to Parts of the discriminative region are erased in an adversarial manner by replacing the response value with zero.

In this step, by partially erasing the input feature map of the auxiliary classifier, the main classifier generates a pedestrian category-specific feature map through step S901, and further according to the part of the body response heat map whose value is higher than the anti-erasing threshold, it is set as discriminative region, and the corresponding region in the input feature map of the auxiliary classifier will be erased by the adversarial way of replacing the response value with 0. The feature maps input by the auxiliary classifier are partially erased, which can increase the variation of feature maps while imitating the occluded pedestrian situation.

As shown in FIG. 9, in step S903, the pedestrian category-specific feature map output by the main classifier and the erased feature map output by the auxiliary classifier are respectively calculated through a loss function, Get the loss value.

As shown in FIG. 9, in step S904, the parameters of the training model are updated according to the loss value.

In this step, both branches of the main classifier and auxiliary classifier are updated under the supervision of the Softmax loss function, and the expression of the loss function is:

代表采用全卷积分类网络时，第p个样本的第m个分支的第k个分类器的第l_p个Softmax输入的节点值，其中l_p是第p个样本的类别。每一个分支的第一个分类器都是主分类器，第二个分类器是辅分类器，参数λ_k是分配给这两个分类器损失的权重，其中参数λ₁＝1对应的是主分类器，参数λ₂＝0.5对应的是辅分类器。Wherein, P represents the size of batch samples, M represents the number of branches, K represents the number of classifiers in the anti-erasure learning (2 in this embodiment), C represents the number of categories,

Represents the node value of the l pth Softmax input node value of the _lpth Softmax input of the kth classifier of the mth branch of the pth sample when the full convolutional classification network is used, where _lp is the category of the pth sample. The first classifier of each branch is the main classifier, the second classifier is the auxiliary classifier, and the parameter λ _k is the weight assigned to the loss of these two classifiers, where the parameter λ ₁ =1 corresponds to the main classifier For the classifier, the parameter λ ₂ =0.5 corresponds to the auxiliary classifier.

In step S130, pedestrian re-identification is performed by using the training model combined with the target pedestrian image and the pedestrian image to be recognized, to obtain a pedestrian re-identification result.

FIG. 10 is a flowchart of step S130 in FIG. 1 according to an embodiment of the present disclosure, which specifically includes the following steps:

As shown in FIG. 10 , in step S1001 , input the image of the target pedestrian and the image of the pedestrian to be identified into the training model for training, and obtain corresponding depth features respectively. In this step, the target pedestrian image and the pedestrian image to be recognized are sent to the CNN model trained in step 2 to extract image features. Specifically, the features of different semantic levels (res_conv5a, res_conv5b, res_conv5c) in Figure 2 are concatenated as the final feature description son.

As shown in FIG. 10, in step S1002, the cosine distance is calculated according to the depth feature of the target pedestrian image and the depth feature of the pedestrian image to be identified, and the calculation formula is:

Where feat1 is the depth feature of the target pedestrian image, and feat2 is the depth feature of the pedestrian image to be recognized.

As shown in Figure 10, in step S1003, the similarity between the target pedestrian image and the pedestrian image to be recognized is determined according to the size of the cosine distance, wherein the pedestrian image to be recognized with the largest similarity is the pedestrian re-identification result.

Since the similarity between the target pedestrian image and the pedestrian image to be recognized is negatively linearly correlated with the characteristic cosine distance, the smaller the characteristic cosine distance, the higher the relative similarity of the graph. Based on the above, the cosine distance can be obtained and sorted in ascending order, that is, the images are sorted in descending order of similarity, and the image of the pedestrian to be recognized with the largest similarity is taken as the result of pedestrian re-identification.

In summary, using the pedestrian re-identification method provided by the embodiment of the present disclosure, on the one hand, by providing a feature-level data enhancement strategy, the input feature map of the auxiliary classifier is partially erased, increasing the variation and resistance of pedestrian features In the case of occluded pedestrians, the generalization ability of the deep pedestrian re-identification model is improved. On the other hand, the spatial attention model in this disclosure integrates the spatial context information into the local features of pedestrians, enhances the spatial correlation of different positions of pedestrians, and at the same time forms a complementary attention model with the channel attention model. The feature maps are revised in two spatial directions, which can better capture discriminative regions. This disclosure proposes a classification model based on a fully convolutional network, which can directly obtain a body response heat map in the process of forward propagation, guide the erasure of discriminative body regions, and realize data enhancement of feature-level data.

It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. Actually, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided to be embodied by a plurality of modules or units.

Through the description of the above implementations, those skilled in the art can easily understand that the example implementations described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of software products, and the software products can be stored in a non-volatile storage medium (which can be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to make a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) execute the method according to the embodiments of the present disclosure.

Other embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, and these modifications, uses or adaptations follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure . The specification and examples are to be considered exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It should be understood that the present disclosure is not limited to the precise constructions which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (6)

1. A pedestrian re-identification method, characterized in that it comprises: The pedestrian CNN feature map is extracted from multiple pictures, including: randomly selecting the plurality of pictures from the training data set; The plurality of pictures are input to a plurality of different semantic layers of the ResNet50 model to extract, and obtain feature maps of a plurality of channels; Using a channel attention module to process the feature maps of the plurality of channels to obtain channel-processed feature maps; Use the spatial attention module to process the spatial context information of the channel-processed feature map at different positions to obtain the pedestrian CNN feature map; use the method of confrontational erasure learning to imitate the discriminativeness of the pedestrian CNN feature map The model training is carried out in the case where the area is occluded, and the training model is obtained, including: The pedestrian CNN feature map is input to the main classifier and the auxiliary classifier respectively for classification training, and the pedestrian category-specific feature map is output from the main classifier and the auxiliary classifier; The auxiliary classifier is an auxiliary classifier added on the basis of resnet50; The main classifier and the auxiliary classifier include the same number of convolutional layers and global average pooling layers, and the number of channels of the convolutional layer is the same as the number of pedestrian categories in the training data set, and the pedestrian Each channel of the category-specific feature map represents the body response heat map when the pedestrian image belongs to different categories; partial erasing is performed in the auxiliary classifier to obtain the feature map after erasing; The partial erasing performed in the auxiliary classifier includes: Determining the area where the heat map value in the body response heat map is higher than the set resistance erasure threshold as a discriminative area; The portion corresponding to the discriminative region in the pedestrian category-specific feature map output by the auxiliary classifier is erased in an adversarial manner in which the response value is replaced with 0; The pedestrian category-specific feature map output by the main classifier and the erased feature map output by the auxiliary classifier are respectively calculated through a loss function to obtain a loss value; updating parameters of the training model according to the loss value; The pedestrian re-identification is performed by using the training model combined with the target pedestrian image and the pedestrian image to be recognized, and the pedestrian re-identification result is obtained. 2. pedestrian re-identification method as claimed in claim 1, is characterized in that, described utilize channel attention module to process the feature map of described multiple channels, obtain the feature map through channel processing comprising: Obtaining a channel feature descriptor according to the feature map of each channel in the feature map of the plurality of channels; Obtaining a channel attention feature map through an activation function operation on the channel feature descriptor; The channel-attention feature map is multiplied by the feature map aggregated by the feature map to obtain the channel-processed feature map. 3. pedestrian re-identification method as claimed in claim 2, is characterized in that, described feature descriptor comprises the statistical value of described multiple channels, and described feature descriptor is:

The statistical value of each channel is:

Where N is the number of channels, n is the number of channels, and A and B are the length and width of the feature map, respectively; The channel attention feature map is: e=σ(W ₂ δ(W ₁ (s))) Where σ and δ represent the Sigmod activation function and the ReLU activation function respectively,

is the weight of the first fully connected layer Fc1, />

is the weight of the second fully connected layer Fc2, and r is the multiple of attenuation. 4. The pedestrian re-identification method according to claim 1, wherein the spatial context information of the channel-processed feature map at different positions is processed by using the spatial attention module to obtain the pedestrian CNN feature Figures include: performing a 1×1 convolution operation on the channel-processed feature map to obtain a first spatial information feature map T and a second spatial information feature map U; Perform matrix multiplication with the transposition of the first spatial information feature map T and the second spatial information feature map U to obtain a spatial attention feature map; performing a 1×1 convolution operation on the channel-processed feature map to obtain a third spatial information feature map V; Perform matrix multiplication operation with the transposition of the third spatial information feature map V and the spatial attention feature map to obtain a spatially processed feature map; Obtaining the pedestrian CNN feature map according to the channel processing and the spatial processing. 5. pedestrian re-identification method as claimed in claim 2, is characterized in that, described using described training model to carry out pedestrian re-identification in combination with target pedestrian image and pedestrian image to be identified, obtaining pedestrian re-identification result comprises: Inputting the image of the target pedestrian and the image of the pedestrian to be identified into the training model for training to obtain corresponding depth features; calculating a cosine distance according to the depth feature of the target pedestrian image and the depth feature of the pedestrian image to be identified; Determine the similarity between the target pedestrian image and the pedestrian image to be recognized according to the size of the cosine distance, wherein the pedestrian image to be recognized with the largest similarity is the pedestrian re-identification result. 6. The pedestrian re-identification method according to claim 5, wherein the calculation formula for calculating the cosine distance according to the depth feature of the target pedestrian image and the depth feature of the image of the pedestrian to be identified is:

Where feat1 is the depth feature of the target pedestrian image, and feat2 is the depth feature of the pedestrian image to be recognized.

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