=Paper=
{{Paper
|id=Vol-3304/paper13
|storemode=property
|title=Pedestrian Attribute Recognition Based on Multi-Scale Feature Fusion Over a Larger Receptive Field and Strip Pooling
|pdfUrl=https://ceur-ws.org/Vol-3304/paper13.pdf
|volume=Vol-3304
|authors=Chen Zou,Wenbiao Xie,Xiaomei Xie,Kai Zhao,Qiuming Liu,He Xiao
}}
==Pedestrian Attribute Recognition Based on Multi-Scale Feature Fusion Over a Larger Receptive Field and Strip Pooling==
https://ceur-ws.org/Vol-3304/paper13.pdf
Pedestrian Attribute Recognition Based on Multi-Scale Feature
Fusion Over a Larger Receptive Field and Strip Pooling
Chen Zou1, Wenbiao Xie1, Xiaomei Xie1, Kai Zhao1, Qiuming Liu1,2 and He Xiao1,*
1
School of Software Engineering, Jiangxi University of Science and Technology, Jiangxi Nanchang 330013, P.
R. China
2
Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications, Jiangxi Nanchang 330013,
P. R. China
Abstract
Pedestrian attribute recognition is a vital task in computer vision, which is widely used in
pedestrian detection and pedestrian re-identification, etc. Pedestrian attribute recognition
aims to excavate the attributes of the target person from video or images. To solve the
specific challenging factors in this task, such as changes in viewpoint, lacking illumination,
and low resolution, we propose a brand new pedestrian attribute recognition method. Using
ASPP to extend the receptive fields and densely connect the generated features, multi-scale
feature fusion merges features from different receptive fields into one feature that is more
discriminative than the input features. And the AIIM module is used to establish remote
dependencies at different spatial scales. Extensive experiments show that our proposed
method achieves state-of-the-art results with average accuracy (mA) of 86.35%, 81.60%, and
83.29% on public datasets such as PETA, PA100k, and RAP, respectively.
Keywords
Convolutional neural network; Atrous spatial pyramid pooling; Multi-scale fusion; Strip
pooling; Pedestrian attribute recognition
1. Introduction 1
Pedestrian attribute recognition[1] aims to predict a set of attributes from a predefined list of
personal attributes ๐ด = ๐ , ๐ , โฏ , ๐ from given images and video, and these attributes are
employed as soft biometric features in visual surveillance system. While in computer vision tasks
such as pedestrian detection[2][3], pedestrian re-identification[4][5][6], and pedestrian retrieval[7][8],
the attribute information can be integrated into computer vision algorithms to achieve better
performance. Therefore, pedestrian attribute recognition is a vital task in computer vision. In
surveillance scenarios, the cameras shoot at different angles and under different lighting conditions,
which can lead to point-of-view problems. All these factors make pedestrian attribute recognition a
challenging task in specific scenes.
Global Image-based models: Such as DeepSAR[9] and DeepMAR[9], etc., take the whole image
as input and perform multi-task learning of PAR. It has been experimentally observed that the
performance of these models is still limited by the lack of fine-grained recognition and the
consideration of correlation between pedestrian attributes.
Part-based models: These algorithms can jointly use local and global information to obtain more
accurate recognition, such as PGDM[10] and LGNet[11]. In PGDM, key points are converted into
information regions by the obtained pose information, and feature learning is performed from each
key point related region using independent convolutional neural network. LGNet proposes a
localization guidance network localizing the regions corresponding to different attributes. The
ICBASE2022@3rd International Conference on Big Data & Artificial Intelligence & Software Engineering, October 21-
23, 2022, Guangzhou, China
chen@jxust.edu.cn (Chen Zou), vebrun@jxust.edu.cn (Wenbiao Xie), mavia@jxust.edu.cn (Xiaomei Xie), kewitt@jxust.edu.cn (Kai Zhao),
liuqiuming@hust.edu.cn (Qiuming Liu), xiaohe804@gmail.com (He Xiao)
ยฉ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
102
shortcoming of such algorithms is that the final recognition performance depends on the accuracy of
localization information, and it increases its training time because of the introduction of human part
localization.
Attention-based models: Methods based on attention mechanisms have become prevalent in
recent years, such as HydraPlus-Net[12], VeSPA[13], etc. HydraPlus-Net is introduced to encode
multi-scale features from multiple levels for pedestrian attributes analysis using multi-directional
attention (MDA) modules. VeSPA take view cues into account and use the attention weights of view
predictions multiplied by view-specific coarse attribute predictions to obtain the final multi-class
attribute predictions to improve attribute prediction accuracy. Guo et al. [14]emphasize the
importance of refining the attentional heat map for each attribute. Although all of these approaches
focus on feature learning, shifting the research focus from the earlier global features to local features
and attention-based features. However, both ignore the study of attribute relevance.
Sequential prediction-based models: JRL[15], RCRA[16], etc., are proposed in this class of
methods that use recurrent learning to obtain the correlation between different attributes to achieve
more accurate prediction results.
Models based on loss functions: In recent years, there are also some loss functions optimized for
PAR improvement, such as WPAL[17].
Figure 1. Sample predictions comparisons, with โ1โ indicating accurate recognition and โ0โ
indicating failure to recognize.
To address the shortcomings of some previous algorithms, we propose a brand new pedestrian
attribute recognition model architecture. As shown in figure 1, the realized boxes in the figure are the
label boxes to be recognized for this sample, and the attributes that StrongBaseline[38] predicts
incorrectly are in the dashed boxes. The attribute labels of the corresponding samples and the results
predicted by the model are shown in the table next to them. As can be seen from the first sample,
StrongBaseline does not accurately identify โNo carryingโ and โShoesโ, and in the second sample,
StrongBaseline does not accurately identify โNo accessoryโ, while our model accurately identifies all
attribute labels. We believe that the reason why โNo carryingโ is not accurately recognized is that the
original model misidentifies the objects in the dashed boxes in the figure. For another problem shown
in the figure, the original model fails to recognize the attributes of โShoesโ and โNo accessoryโ,
which are small detection targets, we solve the problem by increasing the receptive field and fusing
the multi-scale information.
Modeling the contextual relations of different image regions can help to improve recognition
accuracy, but earlier methods may fail to collect global context and capture the long-range
dependencies of different regions due to the limitation of the receptive field[18]. Larger receptive
fields make attribute-specific features easier to recognize. In convolutional neural networks, low-level
features can help capture local area information, while high-level features exhibit global semantic
information. Therefore, by fusing multi-scale features in the feature pyramid architecture[19][20], the
advantages of different levels of features can be exploited.
In this paper๏ผ we propose a new model architecture to extract and fuse multi-scale features over a
larger receptive field and obtain correlations by obtaining global contextual information and capturing
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long-range dependencies in different regions. We conducted experiments on three public datasets
PETA[21], PA100k[12], and RAP[22] to validate the effectiveness of proposed algorithm. We have
made the following contributions to this paper:
โข We propose a multi-scale information encoding module (MSEM) for PAR to extract
multiscale features and encode multiscale information over a larger receptive field.
โข We adopt the feature pyramid model to construct a fusion module (FFM) of multi-scale
features.
โข We propose a new PAR network model architecture that uses stripe pooling as our attribute
information interaction module (AIIM) to capture remote dependencies and learn correlations
between attributes.
โข Extensive experiments on three public datasets, PETA, PA100k, RAP, and ablation
experiments demonstrate the effectiveness of the proposed network framework.
2. Method
2.1. Overall Network Architecture
Figure 2. overall network architecture.
The overall network architecture of this paper is shown in figure 2. Inspired by the feature pyramid,
we use ResNeXt50[23] as the backbone network and construct a network model with a four-layer
feature pyramid structure. The image size is resized to 256 ร 192 and input to the backbone network.
In the backbone network, the output of the feature by the four stages from shallow to deep are
represented as ๐ผ = ๐
ร ร , ๐ โ 1,2,3,4 . The size of the output features ๐ผ is 64ร48, 32ร24,
16ร12, 8ร6, and the number of channels is 256, 512, 1024, and 2048, respectively.
The feature maps ๐ผ , ๐ผ , and ๐ผ obtained above are the input of the multiscale information encoding
module, which is composed in the main part of DenseASPP[24]. The module convalesces the
generated feature maps with different dilation rates of dilation so that the neurons in the output feature
maps contain multiple receptive field sizes that encode multiscale information and feed the output of
each dilation convolution layer to all the unexecuted dilation convolution layers using dense
connections. To construct the feature pyramid structure later as well as to effectively reduce the
network parameters, we extract these feature maps uniformly integrated into 256 channels to obtain
ฮฆ , ๐ โ 1,2,3 . For ๐ผ , we only perform a simple dimensionality reduction to obtain ฮฆ , and again
the number of channels of ฮฆ is 256.
To aggregate information from different spatial scales and fuse features at different scales, we use
a feature fusion module to connect shallow features with features at a deeper level to obtain a more
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discriminative feature map, denoted by ๐ , ๐ โ 1,2,3 , and ๐ is obtained from ฮฆ without any
processing, i.e., ๐ = ฮฆ . Subsequently, to obtain the global information in each feature map and the
correlation between different attributes, the attribute information interaction module was used to
establish the correlation and dependency of the global information. Finally, multiple prediction
outputs are made by the attribute identification module as well as a multi-branch voting mechanism to
obtain the final PAR prediction results.
2.2. Multi-scale Information Encoding Module
Multiscale information[25][26] helps to resolve ambiguous situations and produces more effective
classification. ASPP[26][27] proposes to concatenate feature maps generated by dilation convolution
with different dilation rates so that the neurons in the output feature map contain multiple receptive
field sizes, and this size information encodes multiscale information and achieves improved
performance. ASPP is built on atrous convolution based multi-scale feature generation approach,
multiple dilation convolution features with different dilation rates generate the final feature
representation. Although ASPP can effectively generate multiscale features, its feature resolution on
the scale axis is not dense enough for specific scenes. In addition, in PAR scenes, some attribute
feature information of people varies widely in scale, which requires that the multi-scale information
needs to be encoded correctly for the high-level feature representation in the scene.
Figure 3. Multi-scale Information Encoding Module.
Consequently, DenseASPP[24] for multi-scale information extraction and processing is beneficial
for further feature fusion afterward. As shown in figure 3, DenseASPP consists of multiple dilation
convolutional layers with different dilation rates and a channel compression layer. The output of each
dilation convolutional layer is deeply joined to all the unexecuted dilation convolutional layers. In
MSEM, each dilation convolutional layer uses dilation rates of 3, 6, 12, 18, and 24, respectively. To
reduce the redundant information in the network and for the next step of feature fusion on different
scale spaces, the feature maps compression channel follows the DenseASPP. The specific processing
operation is mainly activated by 3ร3 convolution, BatchNorm2d[36], and Relu functions, the number
of channels is finally set to 256.
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2.3. Feature Fusion Module
In convolutional neural networks, the deep layer network[19] is robust in semantic information
representation and can obtain rich semantic information, while the shallow layer network has a
smaller receptive domain and can capture rich details. By fusing features from different scale spaces
and using different levels of features, attribute associations between features from different scale
spaces can be obtained.
Figure 4. Feature Fusion Module.
FFM is shown in figure 4. In the two adjacent layers of scale features, the deeper feature maps are
summed and fused with the shallow features and finally activated by the sigmoid function as follows:
๐ฅ =๐ ฮฆ +ฮฆ 1
where ๐ฅ is the weight established by feature addition and fusion. ๐ is the sigmoid activation
function. + represents the add operation.
The shallow features adjacent to it are then passed through a convolution block to extract the
information, which consists of a 3ร3 convolution, a normalization and an activation function, with
BatchNorm2d chosen for the normalization and ReLU for the activation function:
๐ฅ =๐ ฮฆ 2
where ๐ stands for convolutional block operation. The above obtained ๐ฅ is multiplied with the
weight vector ๐ฅ , followed by a convolution block to obtain the reweighted feature ๐ :
๐ =๐ ๐ฅ โ๐ฅ 3
At the end, the newly obtained features ๐ are stitched and fused with the features extracted from
the original information of the shallow feature map in the channel dimension to obtain our final
desired feature output ๐ :
๐ =๐ ฮฆ โ๐ 4
where โ is the concatenation.
2.4. Attribute Information Interaction Module
Based on the current research, there are many methods to improve the remote dependency
modeling capability in convolutional neural networks. One is to introduce attention mechanisms, such
as self-attention[28] mechanism, non-local module[29], or Criss-Cross Attention[30], etc., which have
the disadvantage of consuming a large amount of memory to compute a large affinity matrix for each
spatial location. From the convolutional viewpoint, it is also possible to use dilation convolution or
deep separable convolution. However, since these methods probe the input feature map within a
square window, this limits flexibility in capturing contextual information in realistic scenes.
In this paper, the strip pooling method is used to establish the correlation of attribute information
for pedestrians. As described in the paper[31], each positioning element of the output tensor
establishes a correlation with all the position elements that have the same horizontal or vertical
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coordinates as it. Based on the proposed aggregation mechanism, the input tensor of each position
establishes a relationship with other positions, thus outputting a feature tensor with contextual
information established. However, the number of parameters in this method is significantly reduced
compared to other methods[29][30].
3. Experiment results and analysis
3.1. Data set
To validate the effectiveness of the proposed model, experiments were conducted on three public
datasets, PETA, RAP, and PA-100K, respectively.
There are 19,000 images in the PETA[21] dataset containing 8705 pedestrians with resolutions
ranging from 17ร39 to 169ร365. Each pedestrian in this dataset is annotated with 65 attributes (61
binary attributes and 4 multi-category attributes). In experiments, the 19,000 images were divided into
11400 and 7600 for training and testing respectively.
The RAP[22] dataset contains 41585 images collected from real indoor surveillance cameras with
resolutions ranging from 36ร92 to 344ร554. Each image sample contains 72 attributes (69 binary
attributes and 3 multiclass attributes). In experiments, 33,268 images were used for training and 8,317
images were used for testing.
PA-100K[12] is a large pedestrian attribute dataset with a total of 100,000 images collected from
outdoor surveillance cameras, which is labeled by 26 binary attributes. In experiments, it is divided
into 90,000 images for the training set and 10,000 images for the test set.
3.2. Evaluation metrics
For the evaluation of the selected datasets PETA, RAP, and PA100k, two types of evaluation
metrics were used, label-based and sample-based.
For label-based evaluation, the mean accuracy (mA) is adopted. For each attribute, the accuracy is
calculated for all samples (both positive and negative) and then the average of all attributes is
calculated to obtain the mA. The evaluation metric mA is shown below:
1 ๐๐ ๐๐
๐๐ด = + 5
2๐ ๐ ๐
where ๐ is the number of attributes and N is the numbers of samples. ๐ and ๐ are the numbers of
positive and negative cases for the i-th attribute. ๐๐ and ๐๐ are the numbers of positive and negative
cases correctly predicted for the i-th attribute.
For the sample-based evaluation, we use four widely used metrics, including accuracy, precision,
recall, and F1 value, defined as follows.
1 |๐ โฉ ๐ ๐ฅ |
๐๐๐๐ข๐๐๐๐ฆ = 6
๐ |๐ โช ๐ ๐ฅ |
1 |๐ โฉ ๐ ๐ฅ |
๐๐๐๐๐๐ ๐๐๐ = 7
2๐ |๐ ๐ฅ |
1 |๐ โฉ ๐ ๐ฅ |
๐๐๐๐๐๐ = 8
2๐ |๐ |
2 โ ๐๐๐๐๐๐ ๐๐๐ โ ๐๐๐๐๐๐
๐น1 = 9
๐๐๐๐๐๐ ๐๐๐ + ๐๐๐๐๐๐
where N is the number of samples, ๐ denotes the true positive label of the i-th sample, ๐ ๐ฅ
returns the predicted positive label of the i-th sample, and | โ | denotes the set base.
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3.3. Comparison experiments
3.3.1. Comparison with stateโofโtheโart methods.
To demonstrate the effectiveness of RMFA, the performance of RMFA was compared with that of
several state-of-the-art networks, such as ACN[32], DeepMar[9], PGDM[10], LG-Net[11],
HPNet[12], VeSPA[13], JRL[15], MT-CAS[33], MTA-Net[34], WPAL[17], ALM[35], etc. The
experimental results on the PETA and RAP datasets are shown in table 1 and on the PA100k dataset
in table 2. The evaluation criteria mA, accuracy, precision, recall, and F1 are listed. The advantages of
the RMFA can be clearly understood in table1 and table 2.
Table 1. Comparison with state-of-the-art models on PEAT and RAP datasets.
PETA RAP
Methods
mA Accu Prec Recall F1 mA Accu Prec Recall F1
ACN[35] 81.15 73.66 84.06 81.26 82.64 69.66 62.61 80.12 72.26 75.98
DeepMar[9] 82.89 75.07 83.68 83.14 83.41 73.79 62.06 74.92 76.21 75.56
PGDM[10] 82.97 78.08 86.86 84.68 85.76 74.31 64.57 78.86 75.90 77.35
LG-Net[11] - - - - - 78.68 68.00 80.36 79.82 80.09
HPNet[12] 81.77 76.13 84.92 83.24 84.07 76.12 65.39 77.33 78.79 78.05
VeSPA[13] 83.45 77.73 86.18 84.81 85.49 77.70 67.35 79.51 79.67 79.59
JRL[15] 85.67 - 86.03 85.34 85.42 77.81 - 78.11 78.98 78.58
MT-CAS[33] 83.17 78.78 87.49 85.35 86.41 - - - - -
MTA-Net[34] 84.62 78.80 85.67 86.42 86.04 77.62 67.17 79.72 78.44 79.07
WPAL[17] 85.50 76.98 84.07 85.78 84.90 81.25 50.30 57.17 78.39 66.12
ALM[35] 86.30 79.52 85.65 88.09 86.85 81.87 68.17 74.71 86.48 80.16
RMFA(ours) 86.35 79.28 85.16 88.55 86.55 83.29 69.11 77.58 85.23 80.93
While the performance of ALM on the PETA dataset is close to that of RMFA, ALM
underperforms RFMA on both the RAP and PA100k datasets, which demonstrates the better
generalization capability of RFMA. In addition, due to the relatively lower resolution of the images
in the PA100k dataset, it can better present the Motion blur phenomenon in real scenes. As shown in
table 2, the excellent performance of RMFA on the PA100k dataset shows the effectiveness of
expanding the receptive field and fusing multi-scale information. While the PETA and RAP datasets
are labeled with more attributes compared to the PA100k dataset, the data results in the table also
show the necessity and effectiveness of excavating global contextual information and feature
relevance.
Table 2. Comparison with state-of-the-art models on PA100k datasets.
PA100k
Methods
mA Accu Prec Recall F1
DeepMar[9] 72.70 70.39 82.24 80.42 81.32
PGDM[10] 74.95 73.08 84.36 82.24 83.29
LG-Net[11] 79.96 75.55 86.99 83.17 85.04
HPNet[12] 74.21 72.19 82.97 82.09 82.53
VeSPA[13] 76.32 73.00 84.99 81.49 83.20
MT-CAS[33] 77.20 78.09 88.46 84.86 86.62
ALM[35] 80.68 77.08 84.21 88.84 86.46
RMFA(ours) 81.60 78.40 85.16 88.85 86.55
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3.3.2. Attribute Recognition Comparison.
Among all evaluation metrics, mA is one of the vital metrics in model evaluation. As an example,
on the PETA dataset, the mA values of 35 attributes are plotted based on the test results of our model
and StrongBaseline, as shown in figure 5. It can be seen from figure 5 that RMFA improves the
recognition of almost all attributes. In the case of โupper Body Thin Stripesโ, โfootwear Sandalsโ,
โaccessory Sunglassesโ, โupper Body V Neckโ and other fine-grained attributes have improved
significantly, which may attribute to the expansion of the receptive field and the integration of multi-
scale information. The improvement in high-resolution attributes such as โlower Body Casualโ may
attribute to attribute relevance and contextual information excavating.
Figure 5. Comparison of attribute recognition.
In addition, four sample maps were randomly selected from the PETA dataset for recognition
prediction. As shown in figure 6, where the histogram is a comparison of the prediction results of
RMFA with StrongBaseline. The horizontal axis of the histogram is the predicted attribute of this
sample, and the vertical axis is the probability value of the predicted attribute. The prediction
performance improvement of RMFA can be visually shown in figure 6.
Figure 6. Recognition probability of RMFA and StrongBaseline on samples.
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3.4. Ablation experiments
To analyze the effectiveness of key components of the RMFA network architecture, we conduct
ablation experiments on the PETA dataset. The evaluation metrics for this experiment still use the five
important metrics mentioned above, including mA, accuracy, precision, recall, and F1. As shown in
the table, the first row of data in the table indicates the architecture based on our architecture (RMFA)
with the MSEM module removed, the second row of data indicates the architecture with the FFM
module removed, and the third row of data indicates the architecture with the AIIM removed. As the
data in table 3 shows, four of the five evaluation metrics have the highest ranking in proposed
complete architecture. This demonstrates the effectiveness and importance of the MSEM module,
FFM module, and AIIM module in the overall network architecture.
Table 3. Ablation experiments on the PETA dataset.
mA Accu Prec Recall F1
-MSEM 85.90 79.26 85.01 88.68 86.53
-FFM 85.72 73.4 75.84 93.54 82.97
-AIIM 84.61 78.17 84.51 87.45 85.68
RMFA 86.35 79.28 85.16 88.55 86.55
In addition, in this ablation experiment, a comparison with the original overall architecture was
plotted based on the mA values of the properties of the different ablation experimental architectures.
As shown in figure 7, figure 8, and figure 9, the comparison plots of attribute recognition with the
MSEM module removed, the FFM module removed, and the AIIM module removed are shown
respectively. As shown in figure 7, recognition rates of attributes such as โupper Body Logoโ,
โfootwear Sandalsโ, โupper body pinstripeโ, and โupper Body V Neckโ decreased significantly after
removing the MSEM module, and most other attributes also showed a minor decrease in recognition
rates. This shows our previous consideration about the effective impact of expanding the receptive
field. As shown in figure 8, after removing the FFM module, the recognition rate decreases
significantly for the attributes โaccessory Nothingโ, โcarrying Nothingโ, โupper Body Thin Stripesโ,
etc. This experiment shows that the fusion of multi-scale information is effective for fine-grained
attribute recognition. As shown in figure 9, after removing the AIIM module, we can see that the
fusion of multi-scale information is effective in identifying attributes such as โAgeโ, โCasualโ,
โFormalโ, โPersonal Maleโ, etc. โPersonal Maleโ and some other attributes that require global
information judgment produces a decrease in recognition. Also, the decrease in the recognition rate of
some other attributes shows that obtaining global contextual information and attribute relevance also
affects the recognition of other attributes to different degrees, and this shows the effectiveness of the
AIIM module for the overall architecture.
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Figure 7. Comparison of attribute predictions on ablation experiments with MESM removed.
Figure 8. Comparison of property predictions on ablation experiments with FFM removed.
Figure 9. Comparison of property predictions on ablation experiments with AIIM removed.
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4. Conclusion
In this paper, a pedestrian attribute recognition algorithm based on a combination of extracting and
fusing multi-scale features from an expanded receptive field and extracting attribute correlations is
proposed. The MSEM module is used to encode the expanded receptive field and multi-scale
information. In the FFM module, the feature pyramid construct is used to fuse different feature
information from different scales. And finally, the AIIM module is used to obtain contextual
information and attribute relevance. Extensive experiments are conducted on PEAT, RAP, and
PA100k datasets and achieved excellent results. In addition, the ablation experiments demonstrate the
effectiveness of the key blocks in RMFA. The performance of RMFA is significantly improved due to
the cooperation between different modules. In the future, further exploration of the relationship
between multiple attributes to optimize the effectiveness of the AIIM modules can be taken into
consideration.
5.Acknowledgment
This work was supported by the Foundation of Jiangxi Educational Committee (No. GJJ200824)
and the Natural Science Foundation of Jiangxi Province (Grant No. 20202BAB212003).
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