Robust Pedestrian Attribute Recognition for an Unbalanced Dataset
using Mini-batch Training with Rarity Rate
Hiroshi Fukui1 , Takayoshi Yamashita1 , Yuji Yamauchi1 , Hironobu Fujiyoshi1 and Hiroshi Murase2
Abstract— Pedestrian attributes are significant information
for Advanced Driver Assistance System(ADAS). Pedestrian
attributes such as body poses, face orientations and open
umbrella are meant action or state of pedestrian. In general,
this information is recognized using independent classifiers foreach task. Performing all of these separate tasks is too timeconsuming at the testing stage. In addition, the processing
time increases with the number of tasks. To address thisproblem, multi-task learning or heterogeneous learning isable to train a single classifier to perform multiple tasks. In
particular, heterogeneous learning is able to simultaneously
train regression and recognition tasks, because reducing bothtraining and testing time. However, heterogeneous learning
tends to result in a lower accuracy rate for classes with a
few training samples. In this paper, we propose a method toimprove the performance of heterogeneous learning for suchclasses. We introduce a rarity rate based on the importanceand class probability of each task. The appropriate rarity rateis assigned to each training sample. Thus, the samples in amini-batch for training a deep convolutional neural network
are augmented by this rarity rate to focus on the class witha few samples. Our heterogeneous learning approach with therarity rate attains better performance on pedestrian attributerecognition, especially for classes representing open umbrellas.

I. INTRODUCTION
In an Advanced Driver Assistance System (ADAS) [1],object recognition on vehicle camera assists the driver indecision-making under hazardous conditions. Generally, the
ADAS includes pedestrian or vehicle detection and traffic
sign recognition. To prevent collisions between vehicles andpedestrians, these detection technologies are a key function
within the ADAS.
The combination of the histogram of oriented gradient(HOG) and the support vector machine (SVM) is a common
approach for pedestrian detection [2]. The HOG feature
focuses on the gradient of a local region and is robust
to small variations in pose. Following Dalal work, several
related pedestrian detection methods have been proposed [3][4] [5] [6] [7]. With the proliferation of deep learning, theconvolutional neural network (CNN) has become a common
classifier for pedestrian detection [8].
To advance the current state of ADAS, pedestrian attributerecognition is part of the key functions for improvement.
Pedestrian attributes are significant in supporting intelligent
ADAS decisions. For example, the orientations of a pedestrian s body and face are noticeable attributes. Such attributes
are used to predict pedestrian behavior (e.g., aiding the
1 ChubuUniversity, 1200 Matsumoto cho, Kasugai, Aichi, Japanfhiro@vision.cs.chubu.ac.jp

2 Nagoya University, Furo cho, Chikusa ku, Nagoya, Aichi, Japan

Open umbrella: Pedestrian part
(Head, Legs)

Face orientation(Front, Back, Left, Right)

Body orientation(Front,(F
Back, Left, Right)

MaleFig. 1.

FemaleRecognizing pedestrian attributes using heterogeneous learning

ADAS in predicting that the pedestrian will suddenly run infront of the vehicle). As another example, collisions between
vehicles and pedestrians occur more frequently on rainy days
than sunny days [9]. Thus, the attribute signifying weather
a pedestrian has an open umbrella is important in predicting
and preventing these traffic conflict.
The common approach is to train a classifier for eachtask such as recognizing the body or face orientation andother attributes. Unfortunately, this is inefficient because thecomputational cost of training and testing increases with thenumber of tasks. To address this problem, multi-task learning [10] trains a single classifier to carry out multiple tasks.
A CNN trained using multi-task learning has units outputting
the recognition results corresponding to each task. Thus, asingle neural network classifies multiple tasks simultaneously
and the computational cost does not vary according to thenumber of tasks. If it recognizes multiple heterogeneoustasks which regression tasks and recognition tasks, heterogeneous learning is recognized their tasks. Heterogeneous
Learning can train the multiple heterogeneous tasks by selecting error function of regression and recognition. Several
methods have proposed the use of heterogeneous learning[12] [13]. In this paper, we consider multiple pedestrian
attributes including simultaneous recognition and regression
tasks using heterogeneous learning, as shown in Fig. 1.
To train the classifier with heterogeneous learning, it isnecessary to prepare the dataset whereby samples have multiple labels for each task. Typically, the number of samples
for each class of each task is unbalanced and it is difficult
to construct a dataset such that each class of each task
has an equal number of samples. The performance of a
CNN trained by heterogeneous learning tends to significantlyUnits:
3000
Units:
2000

Data Augmentation

Pedestrian partAct. : Linear

Units:
1000

Body orientation
Act. : Softmax

Create a Mini-batch
Conv. : 5x9x32
Act. : Maxout
Size : 60x120x32

Conv. : 3x5x16
Act. : Maxout
Size : 28x56x16

Face orientation
Act. : Softmax

Conv. : 3x5x64
Act. : Maxout
Size : 12x24x64

GenderAct. : Softmax

Data Augmentation

Open umbrella
Act. : Softmax

Size:1x64x128

Input image
Output Layer
Max pool : 2x2
Size : 30x60x16

Max pool : 2x2
Size : 14x28x8

Convolutional Layer

Training dataset

Training dataset(after Data Augmentation)

Fig. 2.

Dropout

Fully connected Layer

Deep Convolutional Neural Network for Heterogeneous Learning

Deep convolutional neural network for heterogeneous learning

deteriorate for classes with few samples. This problem isinherent in the training process of the CNN. Mini-batch is
a common approach used to update the CNN parameters bybackpropagation with the error of the subset which contains
a few training samples. In traditional mini-batch approaches,
the training samples are chosen randomly from the training
dataset. The probability of choosing a training sample froma class with few samples is lower than the probability ofchoosing a sample from a class with many samples. Thus,
the performance of classes which consist a few samples islikely to suffer from difficult to choices the training sample
equally.
In this paper, we propose a new mini-batch selection
approach that introduces the rarity rate of training samples
to alleviate the above problem. First, we use the rarity rate toprepare the subset to balance between the common and rare
sample accordingly. The rarity rate is based on the ratio of
the number of samples in the classes corresponding to eachtask. The probability of choosing the samples from a classwith few samples is increased in the subset as comparedwith traditional mini-batch. The samples chose according
to the rarity rate are subjected to data augmentation toincrease variation. Then, we select mini-batch candidates
randomly from the augmented subset and select one minibatch that appropriately represents the rare samples. The
heterogeneous learning CNN using our proposed mini-batch
creation method improves the recognition performance forclasses with a few samples.
II. P EDESTRIAN ATTRIBUTE RECOGNITION USING AHETEROGENEOUS LEARNING CNN
We categorize related work into pedestrian attribute recognition and heterogeneous learning. In the following subsections, we describe the related methods in these categories
and then further discuss the problems with existing heterogeneous learning CNN methods as applied to pedestrian
attribute recognition.
A. Related pedestrian attribute recognition work
Pedestrian detection is part of an important ADAS function. Dalal proposed an impressive pedestrian detection
method that employs HOG and SVM [2]. The HOG extracts
the gradient value rather than pixel values and is thus robust

to pedestrian appearance variations. The features derived
using HOG has been proposed in many publications [3] [4][5] [6].
Since Krizhevsky successfully achieved object recognition
using CNN [14], the deep learning architecture has become
widely applied in computer vision and pedestrian detection
in particular [8]. Hosang used AlexNet, a common deeplearning architecture, for pedestrian detection and achieved
impressive performance [8]. The aforementioned work also
analyzed the performance for a number of training samples.
Pedestrian attribute recognition is also one of the most
key function in ADAS and is a significant part of reducing
collisions between vehicles and pedestrians. Ricci proposed amethod to estimate the body and face orientation from RGB
and stereo images [15]. This method employs a dynamic
Bayesian network for estimation and each input region is
extracted using tracking on a sequence of stereo images.
Bearman proposed human pose estimation and joint position
detection using CNN [16]. While this method attains highpose estimation performance for complex human poses,
the networks for body pose detection and joint position
estimation are constructed individually.
B. Heterogeneous learning
Performing recognition or estimation for multiple tasks
requires the construction of classifiers corresponding to eachtask, as shown in Fig. 2. This is time-consuming during
training and testing, and the computation time increases
with the number of tasks. One of the methods developed
to address this problem is heterogeneous learning. Heterogeneous learning performs multiple tasks in a single network.
A CNN trained for heterogeneous learning has units that
output the recognition results corresponding to each task. The
computational cost does not directly depend on the numberof tasks. Zhang proposed a method to perform multiple tasks
such as facial point estimation, gender classification, faceorientation estimation, and glasses detection [13]．While
the method estimates multiple task, its main purpose is toimprove the performance of the primary task, such as facial
point detection. It thus assigns weighted loss functions toeach task. When the loss decreases sufficiently, the trainingof the task is terminated earlier to avoid over-fitting to a
specific task.

(1)Regression
Em,t=∥ y − o ∥22(2)

W + ∆W!M !T∂ m=1 t=1 Em,t= W−η∂W
C. Drawbacks of the heterogeneous learning CNN
W

40

20

20

0

Front

←

(3)

A CNN trained by heterogeneous learning typically shows
poor performance for classes with few samples. Figure 3
shows the relationship between the ratio of the number oftraining samples and the accuracy for each task. The accuracy
for the classes with few training samples is lower than for
those with more training samples.
This happens characteristic of the training process of the
CNN. The samples belonging to classes with many samples
are selected in the mini-batch and the error is propagatedfrequently. However, the error of a class with a few samples
is rarely propagated. As a result, the performance of thesesmall classes deteriorates. This problem corresponds to thedifferent sample distributions in each class of each task.
III. P ROPOSED METHOD
To improve the performance for classes with few samples,
we propose a method using a rarity rate assigned to eachsample. The samples forming the mini-batch are choosing
based on rarity rate. Note that we refer to samples fromsmall classes as rare samples and samples from large classes
as common samples. In conventional mini-batch creation, theperformance for the rare classes is worse because of the

Left

0

(a) Body orientation recognition

100

Accuracy[%]

Right

Back

100

80

80

60

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40

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0

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Right

Back

(b) Face orientation recognition

Left

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Male

FemaleNo

Yes

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Ratio of number oftraining samples[%]Accuracy[%]8060(c) Gender recognition

Note that, o and y mean labels and response for eachtask, respectively. The errors Em,t of the sample m for
all tasks {t|1, . . . , T } are accumulated and propagated once
per iteration in Eq.(3). The parameters W of the CNN
are updated by using the differential of the accumulated
error with the training coefficient η. In this paper, we use
a heterogeneous learning CNN for pedestrian part detection,body orientation, face orientation, gender, and the presenceof an open umbrella, as illustrated in Fig 1.

100

80

Ratio of number oftraining samples[%]Classif ication
Em,t= −y log o：Recognition rate
：Ratio of number of training samples

100

Accuracy[%]During the pre-processing to train the CNN, the training
samples are augmented. Data augmentation is a common
strategy to increase the number of training samples usingtranslation, scaling, and rotation. After data augmentation,
M training samples are chosen randomly to form the minibatch. In mini-batch training, the error E is calculated andbackpropagated to update the parameters of the network. At
each backpropagation [18] iteration, the samples in the minibatch are selected randomly from the augmented dataset.
When the CNN is trained with heterogeneous learning, therecognition and regression tasks are combined in a single
network and each task has an independent loss function. The
cross entropy in Eq.(1) and the mean squared-error in Eq.(2)are employed as the loss functions of the recognition andregression tasks, respectively.

(d) Open umbrella recognition

Fig. 3. Relationship between the number of training samples and accuracy
for each task

random choice of training samples. The proposed method
improves the performance for these classes by increasing thenumber of choice times for rare samples by using the rarity
rate.
First, we define the rarity rate for each training sample {n|1, . . . , N }. Then, the training samples n are augmented by the rarity rate. Using the rarity rate, the rare
samples are augmented to many samples．In contrast, theaugmentation of common samples is suppressed to a few
samples.
After data augmentation, the samples are chosen randomly
to form the mini-batch. We create several mini-batches as
mini-batch candidates and select one that has an appropriate
sample balance. The network is trained and its parameters
are updated using the selected mini-batch. New mini-batch
candidates are created and selected at each iteration. The
following subsections provide detailed information about
these algorithms.
A. Definition of rarity rate
The rarity rate of a training sample n is a quantitativevalue signifying the rarity of the corresponding class in eachtask. Figure 4 shows the process to assign the rarity rate toa training sample. Each training sample has labels for eachtask. In this case, choosing training sample has four labels
which are left body orientation, left face orientation, female
gender, and no umbrella. These ratios of the training samples
in each class for the task t are calculated from these attributelabels of all training samples. The rarity rate Rn of a training
sample is defined by Eq.(4).
Rn =

T
−1
"
t=1

(1 − pn,t ) ·

#

1−

pmin
t
/T
pmax
t

(4)

Choosing training sample

100

80

80

60

60

60

31.03%

40

31.63%

40

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40

60
40

M

Face orientation
recognition

No

e
al
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m

t
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f

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on

t
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al
e

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t

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gh
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Body orientation
recognition

92.21%

80

42.76%

s

100

80

Ye

100

Fr
o

Ratio oftraining sample[%]Body orientation : Left
Face orientation : Left
Gender : Female
Do not open umbrella

Open umbrella
recognition

Gender recognition

Calculating the rarity rate

Rarity rate : 0.33

Training Dataset

Calculating the ratio oftraining sample for each task

Fig. 4.

Definition of the rarity rate

Note that pn,t is the ratio of training samples in the corresponding class for task t. pmin
and pmax
are the minimumtt
and maximum ratios of training samples, respectively.
The first term in Eq.(4) indicates the rarity rate of thecorresponding class. The second term indicates the deviation
between classes in the task t.
If a training sample belongs to a class with a few training
samples, the rarity rate of the first term increases. In contrast,
if the training sample belongs to a class with many training
samples, the rarity rate of the first term decreases. The
deviation of the training samples of the task is obtained fromthe ratios of training samples in each class. It is defined by
the classes that have the minimum and maximum amounts oftraining samples. To suppress the deviation between classes,
the ratio of the class with the most training samples isnormalized to one.
B. Data augmentation considering the rarity rate
Because the random choice probability of rare samples
is lower than that of common samples, traditional minibatch creation severely under-represents rare samples. Whiledata augmentation can increase the number of samples, bothrare and common samples are increased equally using a
conventional approach. Thus, our proposed method applies
data augmentation based on the rarity rate. We thereby
augment rare samples and suppress the augmentation ofcommon samples.In addition, we do not do transformations
such as mirroring which changed especially belonging classfrom data augmentation. This can result in an augmented
dataset that includes equal numbers of training samples foreach class for each task. The augmented number of samples
Sn is defined using the rarity rate Rn as shown in Eq.(5).
Sn = R n · A + 1

2.11
Choose training samples

Mini-batch 1

3.02
Choose training samples

Mini-batch 2

4.16
Choose training samples

Mini-batch K

Fig. 5.

Creating the candidate mini-batchesaugmented samples corresponding to the rarity rate. The
probability of rare sample choice is thus increased.
C. Selecting the mini-batch candidate
To select the mini-batch with the appropriate sample
balance, we create several mini-batch candidates. Each minibatch candidate is constructed by choosing samples from theaugmented dataset described in the previous subsection. The
rarity rate for mini-batch candidate RkB is defined from thetotal rarity rates of the chose sample Rm as shown in Eq.(6).
K mini-batch candidates are considered preparing with therarity rate.
RkB =

M
"

Rm

(6)

m=1

Mini-batch candidates are sorted by their rarity rates RkB
and the one with the median rate is selected. Mini-batch
candidates with high rarity rates include many rare samples,and mini-batch candidates with low rarity rates include many
common samples. The mini-batch with the median rarity ratebalances between rare and common samples.

(5)

A is the maximum augmentation number for increasing
the training samples by data augmentation. It suppresses
the augmentation of common samples by the number ofRarity rate at
Mini-batch

IV. E XPERIMENTS
A. Overview
We evaluate our method for multiple tasks with datasetsthat are unbalanced in the number of samples for each class.

100

Recognition ratio[%]

90
80
70
60
：CNN of Single task(M=5)：CNN of Heterogeneous Learning(M=5)

50：CNN of Heterogeneous Learning(M=10)：CNN of Heterogeneous Learning(M=20)：Proposed Method(M=5)

40：Proposed Method(M=10)

Body orientation recognition

Fig. 6.

ra
ge
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l t ge
as o
ks f

Av
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s
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ge

e

Gender recognition

No

ra
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Face orientation recognition

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ft
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ag
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on
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Pedestrian part detection

Fr

Av
e

tl
eg
gh
Ri

ft
l
Le

ad
He

eg

：Proposed Method(M=20)30Open umbrella
recognition

Comparison between conventional methods and proposed method

In the experiments, we compare the performance of proposed
method and conventional methods. Note that, we preliminarily evaluate the parameter of our method are the maximum
number of augmentation samples A and the number of minibatch candidate K. Once the optimal parameters are used, we
compare the performance of our method with conventional
methods. The comparison methods are:
• Recognition of each task using individual CNNs,• Recognition of all tasks by a single CNN trained byheterogeneous learning,• Recognition of all tasks by the proposed method.
To compare performance, we evaluate all methods withvarying mini-batch sizes M of values {5, 10, 20}. The
comparison dataset consists of 82,364 pedestrian images thatwere taken from a vehicle camera. In addition, we divide
training samples of 45,581 and test samples of 36,783 inthis dataset. Each pedestrian image is labeled for five tasks:
pedestrian part position, body orientation, face orientation,
gender, and open umbrella detection, as illustrated in Fig.1.
These ratios of training samples in each task are shown in
Fig.5. The class with the least samples is open umbrella
in all of the tasks. The performance of each task except
pedestrian separate detection is evaluated according to therecognition rate. For pedestrian part detection, we evaluate
the localization error as a fraction of the head-to-legs distance(this is invariant with respect to the actual size of the images).
A point has been correctly detected if the pixel error is lower
than 10% of the head-to-legs distance.
In this experiment, we employ a CNN that consists ofthree convolutional layers and three fully connected layers, as
shown in Fig. 2. The total number of iterations to update theparameters is 500,000, and the training ratio η is set to 0.001.
We evaluate each method five times to smooth variability andcalculate the standard deviation. In addition, we set the same
initial parameters for all methods.
B. Experimental result
In Fig. 6, we compare the performance of our proposed
method with single task learning and heterogeneous learning.

We show the recognition rates as the parameter M varies
within {5, 10, 20}. In our method, we set to A = 25 and K =15 why are best performance in preliminarily experiments
which changed the parameters A and K, respectively.
Figure 6 shows that the CNN with heterogeneous learning
is superior the single task approach for pedestrian partdetection. In particular, the detection performance of the
CNN with heterogeneous learning improves by more than
10% for both legs as compared with the single task approach.
The body orientation attribute is supported by part detection,
especially the legs, because the part position and bodyorientation recognition can be performed simultaneously. The
proposed method improves the performance for rare classes
such as open umbrella , body orientation is front , faceorientation is front , and female gender by applying therarity rate to heterogeneous learning. When we evaluate thevarious values of the mini-batch size M , the conventional
heterogeneous learning achieves its best performance withM equal to 20. Our method achieves its best performancewith M equal to 5.
In Fig. 7, we show the recognition results. The circles
at the head and foots denote the orientation of the faceand body, respectively. The green points are detected part
positions in the head and legs. The color of the bounding box
indicates gender (blue is male and red is female). In addition,
when an open umbrella is detected, an umbrella icon isshown on the right-bottom. As shown in Fig.7, both conventional heterogeneous learning and the proposed method
can recognize multiple tasks for various pedestrian poses.
However, the conventional method has difficulty recognizingclasses with few samples such as female pedestrian , bodyorientation is front , face orientation is front , and open
umbrella . The proposed method improves the performancefor these classes. In particular, the class open umbrella
is significantly improved with respect to the conventional
method.

Conventional method Proposed method

Conventional method
Fig. 7.

Proposed method

Miss recognition

Comparison of example pedestrian attribute recognition using the conventional method and proposed method

V. C ONCLUSION
In this paper, we propose a method to improve the performance of heterogeneous learning of multi-task pedestrian
attribute recognition for classes with few samples. We assign
a rarity rate to each training sample to determine the numberof augmentations such that rare samples are augmented
more. In addition, we create multiple mini-batch candidates
from the augmented datasets and the candidate that balances
appropriately balances between common and rare samples isselected as the training mini-batch. As a result, the proposed
method improves the recognition performance for classes
with few sample. By introducing the rarity rate during data
augmentation, the proposed method reduces the number oftraining samples by half.
ACKNOWLEDGMENT
This research is partially supported by Center of Innovation Program from Japan Science and Technology Agency,
JST.
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