Scene-Specific Pedestrian Detection Based on
Parallel Vision

arXiv:1712.08745v1 [] 23 Dec 2017

Wenwen Zhang, Kunfeng Wang, Member, IEEE, Hua Qu, Jihong Zhao, and Fei-Yue Wang, Fellow, IEEE

Abstract—As a special type of object detection, pedestrian
detection in generic scenes has made a significant progress trained
with large amounts of labeled training data manually. While
the models trained with generic dataset work bad when they
are directly used in specific scenes. With special viewpoints,
flow light and backgrounds, datasets from specific scenes are
much different from the datasets from generic scenes. In order
to make the generic scene pedestrian detectors work well in
specific scenes, the labeled data from specific scenes are needed to
adapt the models to the specific scenes. While labeling the data
manually spends much time and money, especially for specific
scenes, each time with a new specific scene, large amounts of
images must be labeled. What’s more, the labeling information
is not so accurate in the pixels manually and different people
make different labeling information. In this paper, we propose
an ACP-based method, with augmented reality’s help, we build
the virtual world of specific scenes, and make people walking in
the virtual scenes where it is possible for them to appear to solve
this problem of lacking labeled data and the results show that
data from virtual world is helpful to adapt generic pedestrian
detectors to specific scenes.

(a)

(b)

(c)

(d)

Fig. 1: Examples of surveillance equipments in specific scenes: (a) CMU
indoor vision research (b) sub-open scene-subway station (c) MIT surveillance
dataset from traffic scene (d) Town center scene.

I. I NTRODUCTION
Detecting pedestrians from video sequences in specific
scenes is very important to surveillance of traffic scenes and
other public scenes such as squares or super markets [1]. It
can be used to analyze abnormal behaviors of pedestrians
in order to manage the crowd or prevent unexpected events.
As Fig. 1 shows, there are many scenes (specific scenes)
equipped with static cameras to record the events happened,
followed by understanding the content of the captured videos
and extracting useful information from the video sequences.
This work was partly supported by National Natural Science Foundation
of China under Grant 61533019, Grant 71232006, and Grant 91520301.
Wenwen Zhang is with School of Software Engineering, Xi’an Jiaotong
University, Xi’an 710049, China, and also with The State Key Laboratory
for Management and Control of Complex Systems, Institute of Automation,
Chinese Academy of Sciences, Beijing 100190, China (e-mail: zhangwenwen1000@gmail.com).
Kunfeng Wang (Corresponding author) is with The State Key Laboratory
for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, and also with
Qingdao Academy of Intelligent Industries, Qingdao 266000, China (e-mail:
kunfeng.wang@ia.ac.cn).
Hua Qu is with School of Software Engineering, Xi’an Jiaotong University,
Xi’an 710049, China.
Jihong Zhao is with Department of Communication and Information
Engineering, Xi’an Jiaotong University, Xi’an 710049, China.
Fei-Yue Wang is with The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, and also with the Research Center for Computational Experiments and Parallel Systems Technology, National University of
Defense Technology, Changsha 410073, China (e-mail: feiyue@gmail.com).

In this process, pedestrian detection is the prerequisite for the
subsequent event identification.
In recent years, with the improvement of computing power,
machine learning, especially deep learning [2] has been playing a critical role in many topics of computer vision and makes
a great progress, such as in image classification and object
detection.
As a special object detection, pedestrian detection in generic
scenes have achieved a great progress with large dataset trained
detectors , while the pedestrian detectors in generic scenes
work bad when they are directly used for pedestrian detection
in specific scenes because of different geometry information,
viewpoint, illuminations, resolutions, background and so on
[3] between specific scenes and generic scenes.
Although the generic pedestrian datasets presents intra-class
variations, each of the datasets still has its own inherent bias
for the different methods collecting the data. For example,
since the INRIA [4] dataset is taken from the personal digital
images collections, many of the people in the dataset are
intent to make poses facing the cameras [5]. This may be
different from the natural pedestrian poses and actions from
real-life situations. For the dataset of Daimler and Caltech [6],
the pedestrians are captured from the on-board cameras from
vehicles, with different angles and view-points.
In fact, the objective of labeled dataset is to provide the
learned classifier with large inner-class variations of pedestrian

Fig. 2: The process of rebuilding virtual scene and generating synthetic data with accurate labeling information. A. scene rebuilding and simulate pedestrians
in scene(red box): (1) rebuild scene from geometry information (2) simulate pedestrians (3) generate synthetic data from virtual scene. B. Labeling information
Generated(blue box): (1) get the location information of pedestrian in photo through vertex render from 3d model. (2) obtain the final bounding box through
computing.

and non-pedestrian so that the resulting classifier can be
generalizable to never-before-seen test dataset [5]. However,
the pedestrians of generic scene and specific scenes with
different angles, viewpoints and backgrounds are in different
variable spaces, the pedestrian detectors in generic scenes
works bad if they are used to the specific scenes directly.
For appearance-based pedestrian detectors, a large amount
of data is important to train the appearance models.A large
amount of data happens to be lacked for the high cost to obtain.
It is necessary to adapt pedestrian detection models in
generic scenes to specific scenes in order to achieve a better
result, so called ‘domain adaption’ [5]!
So far, there’s much work done in applying the specific
scene pedestrian detector to generic scenes. Wang et al.
[7] uses transfer learning to transfer the generic pedestrian
detectors to the specific scenes with little target labeled data.
Hottori at al. [8] make the 3d models as the proxy of the real
world and train hundreds of classifiers in the scene of each
possible position. Besides those, some researchers combine the
synthetic and real data to train the specific pedestrian detector
to overcome the lack of data. Different from Hattori et al.’s
idea, we directly rebuild the virtual world and simulate the
pedestrian in the virtual scene with real background, and then
collect a large amount of labeled synthetic data.
In this paper, our main goal is to solve the problem of
lacking labeled data in the specific scene based on the parallel
vision theory.
Parallel Vision theory was proposed by Wang et al. [9],
[10], [11] based on ACP (Artificial systems, Computational
experiments, and Parallel execution) theory [12], [13], [14],
[15] attempting to solve the vision problems in the real world.
Parallel Vision System follow the idea of ”We can only under-

stand what we create ”. For parallel vision, A(CP) references
to building the virtual world corresponding to the related real
world, then collecting data from the virtual world; (A)C(P)
references to obtain knowledge of the real world through
Computing Experiments, finally applying the knowledge to
both virtual world and real world to evaluate the effectiveness
of the algorithm, namely Parallel Execution in (AC)P.
Based on the idea of parallel vision, we mainly focus on
building the virtual scene and generating a large amount of
synthetic data from the virtual scene, and then we execute the
Computational experiments with the generated data for pedestrian detection.With the virtual scene, we can generate much
data for Computational experiments with labeling information,
what’s more, the virtual scene can free researchers from the
annoying process of preparing data and make them focus on
algorithms. The process of data generating is showed in Fig.
2, more details can be found in Section 3.
Our contributions is proposing a method for training pedestrian detectors in specific scenes when new surveillance equipments are set up or there is no labeled data in a specific
scene. And there are two purpose in this paper (1): building
virtual scene for specific scenes to generate synthetic data with
labeling information and (2): validating that the synthetic data
is helpful to train a specific scene pedestrian detector, namely
Computing Experiment in ACP theory.
The remainder of this paper is organized as follows:Section
II describes the related works. The virtual world are built,
together with the process of generation of dataset and ground
truth bounding boxes in Section III. In Section IV, experimental results show that the synthetic data from virtual world is
useful in training the pedestrian detectors in the real world
dataset. At last but not the least, Section V is the conclusion.

II. R ELATED W ORK
As a part of ACP method,the virtual scene, also as virtual
world is important. Changing the motivation and appearance
of target objects can generate a large mount of labeled data
just as collecting from the real world! In such way, data-driven
method can easily achieve the hoped goal.
As described by Bainbridge [16], based on Video Game and
Computer Game, the virtual world can simulate the complex
physic world in vision and offers a new space for scientific
research. In fact, as part of parallel vision, the idea of using
synthetic data generated from 3D models and virtual scenes
for object detection is not new.
So far, there’s much work been done based on the virtual
world. Here, we mainly talk about some applications related
to our work.
Virtual Scene In order to generate synthetic data, the first
step is to build up the virtual scene. With the development
of GPU computing power, computer game and 3d model,
building up a virtual scene based game engine and 3d model
software like 3ds max, blender and so on becomes easy.
Prendinger et al. [17] built Virtual Living Lab for traffic
system simulation and analysis of driver behaviors based on
OpenStreenMap, CityEngine and Unity 3D et al. Computer
Game and 3d model softwares. In the Virtual Living Lab, they
generated the traffic road net based on the free map data,and
sense the context based on the interaction between the agent on
board and the agent at the roadside. Karamouzas and Overmars
[18] use the virtual scene to evaluate the proposed pedestrian
motivation model. The virtual scene is also used for camera
networks research [19], [20].
In our work, the main goal is to apply parallel vision
concepts in specific scene, it is important to rebuild the same
scene of the specific scenes, so we directly put variable pedestrians in the image scene based on the technology of augment
reality, which is similar to the the work done by Hattori et al.
[8], synthetic data generated based on the background of the
specific scene.
3D Model for Detection For the similarities between real
life object and virtual object in appearance and motivation,
many researchers attempt to build a virtual proxy to stand
for the real life object and achieve good results. Brooks
[21]described a model-based vision system to model image
features. Dhome et al. [22]presents a method to estimate the
spatial attitude of articulated object from a single perspective
image. There’s much work done in modeling human body
shape [23], pedestrian pose [24], hand gesture[25] and so
on. Hattori et al. [8] use 3ds max model to train pedestrian
detectors at each possible pixel in specific scene with static
camera, and Marin et al. [26] train a pedestrian detector in
mobile scenario using virtual model.
In our artificial scene, we also model the pedestrians from
3ds Max. First, the model from modern 3d model software
is similar to the real life pedestrians; what’s more, with the
synthetic data, we can easily obtain large mount of groundtruth labeled data automatically.

(a)

(b)

Fig. 3: Output different formated image with solid color(black or white)
background for following labeling work. (a) output with black-white object
and background, (b) output with different color for different pedestrian to
show the overlapping relationship.

Scene Adaption In order to train a powerful model of
pedestrian detection,generic data are collected through all
possible way to cover all the possible shape and appearance
of pedestrian.However, all the existent dataset are not satisfy
the principal and goal, for the variable scenes with variable
viewpoint, lighting conditions and so on [7]. In the specific
scene, more effort needs to adapt the pedestrian model to a
specific scene. So adapting the pre-trained models to a new
domain has been an active area of research [7], [27].
Our work is also to adapting a generic pedestrian detector to
a specific scene without real data, similar to their work [8], but
we are not train many detectors in each possible position. We
train the generic pedestrian detector with synthetic data, and
make sure that it is possible to use synthetic data generated
from virtual world as a proxy of the real life.
III. S YNTHETIC DATASET A ND M ETHODS
Fig. 2 gives a pictorial illustration of the process of generating the synthetic dataset and labeling information from the
virtual scene. And we evaluate the synthetic data with DPM
[28] and Faster R-CNN [29] methods.
A. Synthetic Dataset
In this paper, we use 3ds max to rebuild the virtual scene
with known geometry information. With the virtual scene in
3D model, then we place the pedestrians in target scene and
simulate pedestrians walking in the scene illustrated in red box
of Fig. 2.
3D simulation With the geometry information and background of the specific scene, we can easily rebuild the virtual
scene, together with the location of obstacles in 3ds max. In
the built virtual scene, we simulate 3d pedestrians in the scene
with different appearance walking around the virtual scene
with arbitrary possible direction, walking speed and styles.
In 3d simulation, we try our best to make the artificial scene
photo realistic using the technology of AR. So we render
pedestrians walking in different directions, walking around
nearby, talking with each other within a group of two or three,
making a call alone and so on. With different configurations,
we can easily generate variable data from virtual scene to learn
a pedestrian model for the truth.
Ground-Truth Data An important property of synthetic
data is that we can conveniently obtain the ground truth

data for the purpose of training the model and validating
the model. In our work, we obtain the labeling information
from 3d information of virtual scene directly! Although many
researchers realize that it is possible to use synthetic data to
train the real life model, and prove this method is effective in
a way, they ignore the importance of generate the labels from
the synthetic data.
In Sun et al. [30], they generate synthetic images that there
is only one object in each image. To get the labeling information, a parallel set of images was also generated which share
the same rendering setting as the synthetic image generated for
train the model except that the background is always white.
The parallel generated images with white background are only
used in automatically calculating the bounding box of nonwhite pixels, aka the target object bounding box in the image.
It is easy for only one object in the image, and many other
works [31], [32] related with virtual object also generate labels
with the similar method. The method is effective for one object
in the image, but it does not work for images of multi-objects.
For Multi-objects in the image, if the objects are not blocked
each other, it is easy to label each object through search the
connected t domain in the image [33]. But there is no effective
method for the blocked objects. It is impossible to obtain labels
from image processing. At the first time, we also try the exist
method to generate labels from the synthetic data, but we failed
finally.
In the virtual world, we can easily obtain each parameter
in the scene. Along with the render process, the points in the
world XW mapping to the camera coordinates XC , then the
image physics coordinates system Xscreen and final in the
image coordinates Ximg . The hole process can be display as
equation.
From world coordinate system to camera coordinate system:
 
 
 
xw
xw


xc
 yw 
 yw 
R
t
  = M1  
 yc  =
 zw 
0 1  zw 
zc
1
1
From camera coordinate system
tem:

 
f 0
u
zc
= 0 f
v
0 0

to physic coordinate sys 
0 xc
0   yc 
1
zc

Finally, convert the physics image system to image coordinate
system:

u = xu /dx + u0
v = yu /dy + v0
The final labeling information comes from the coordinates
of image, the blocked relationship lies on the relationship of
z-coordinates in camera.
In our work, we generate the synthetic data from virtual
scene, meanwhile a parallel set of vertexes for each pedestrian
is generated. From pedestrians’ vertexes coordinates, we can
easily obtain the labeling information showed as red box of
Fig. 2.

B. Object Detection Methods
The synthetic data is easy to obtain from 3d model together
with labels. In order to validate the effectiveness of the
synthetic data, we work on the data with Faster R-CNN
[29] and DPM (Deformable Part-based Model) [34] pedestrian
detection models, one for appearance-base method from CNN
method and one for edge-based method from HOG-SVM
pedestrian detection.
DPM The DPM (Deformable Part-based Model) model
comes from PS (pictorial structure) [35], the relationship of
different parts, and HOG (Histogram of Graphic) Feature [4],
the features of part characteristic, mainly describing the shape
features of detected object and achieve a great success in object
detection in the challenge of PASCAL VOC. The authors of
DPM, Pedro Felzenszwalb et al. were awarded a “lifetime
achievement” prize by the VOC organizers, which reflect the
importance of DPM pedestrian detection method. The code
can be found at https://github.com/rbgirshick/voc-dpm.
Faster R-CNN Different from DPM model, Faster RCNN [29] is a appearance-based method coming from
CNN.Recently, the CNN methods have achieve a great process
in the vision problems as we mentioned above. As one instance
of deep learning, Faster R-CNN, combining Fast R-CNN [36]
with RPN (Region Proposal Network), makes one stack object
detection model and performances well. The python code can
be found at https://github.com/rbgirshick/py-faster-rcnn.
DPM and Faster R-CNN are two different methods to
describe the detected object and they all perform well in object
detection. We evaluate our proposed idea on both DPM and
Faster R-CNN method, and the result show that the synthetic
data is useful in object detection.
IV. E XPERIMENTAL E VALUATION
A. Dataset
In order to validate the effectiveness of virtual scene for
generating data in pedestrian detection, we evaluate it in
different scenes as Fig. 4.
Town Center Dataset [37]: In our work, we mainly work
on the Town Center dataset, a video dataset of a semi-crowded
town center with a resolution of 1920*1080 and a frame of 25
fps. In order to consume the computing time and convenient
to process, we also down-sample the videos to a standardized
resolution of 640*360, together with the ground-truth labels
as Hattori et al[8]. The original Town center Dataset only
provides the validation set, and we use it for test dataset in
our experiments.
Atrium Dataset [38]: Atrium pedestrian dataset was filmed
at cole Polytechnique Montral. It offers a view from the
inside of the building and we can see pedestrians moving
around crossing each others. This movie is intersecting since it
allows to evaluate the performance on pedestrians. The movie
resolution is 800x600. For the evaluation, 4540 frames (30
fps) of the movie were annotated.
PETS 2009 Dataset [39]: The PETS 2009 dataset consists
of videos(at a resolution of 720X576) of campus from Kyushu

(a)

(b)

(a)

(b)

(a)
(a)

(b)
(b)

curve show that the both Faster R-CNN and DPM model works
well when trained with the synthetic data generated from the
virtual scene of Town center built by us than the models trained
with general dataset of pascal voc 2007. From the AP of
different method in different scene evaluated datasets, we can
conclude that in a specific scene, it is useful to train the model
with synthetic data generated from virtual world.
Table I shows the increment of AP with synthetic from
virtual scene compared to generic data of pascal voc 2007
in each evaluation scene for different method. In scene Town
Center and Atrium, the synthetic data improve the model much
by a large margin, while the PEST 2009, for the pedestrians in
the scene are much similar to the pedestrians in pascal voc, the
generic model of DPM and Faster R-CNN have achieved good
results. However, the specific models trained with synthetic
data can still achieve results are not worse than them at least.
V. C ONCLUSION

(a)
(a)

(b)
(b)

Fig. 4: The Evaluation Scenes with their corresponding geometric information
and pedestrians simulated in them. Town Center (top), Atrium (middle) and
PETS 2009 (bottom).

University including a number of pedestrians.While the dataset
consists of videos captured from 8 different views, we just use
a single 8th camera view for our experiments. The ground truth
information of pedestrians are labeled by ourselves with the
tool labelimg from github 1 .
B. Baselines
We evaluate the efficiency of the virtual scenes and compare
against with the following baselines.
D-V/S: D-V and D-S stand for the DPM model of pedestrian
detection trained with pascal voc 2007 dataset and synthetic
dataset generated from the virtual world built by us respectively.
F-V/S: The same with D-V/S, F-V stands for the Faster RCNN pedestrian detection model trained with dataset of person
from pascal voc 2007 and the F-S stands for the Faster RCNN model trained with the synthetic data generated from
the dataset from the virtual scene.
All above are all have the same test set from the baseline
of specific scene pedestrian detection datasets: Town Center
Atrium and PETS 2009. Except the experiments above, we
also train and test Faster R-CNN and DPM model with person
dataset from pascal voc 2007.
C. Results
We compare Faster R-CNN and DPM model to the baseline
of datasets of all selected specific scenes based on pascal voc
2007 evaluation metric for pedestrian detection.
The PR curves of pedestrian detection on the selected specific datasets are summarized in Fig. 5, and the precision-recall
1 labelimg:

https://github.com/tzutalin/labelImg

We propose a parallel vision approach to pedestrian detection by building the virtual world to generate a large amount
of synthetic data and adapting the generic model to specific
scenes. The experimental results show that synthetic data is
able to learn a scene-specific detector without real labeled data.
In this paper, the concerned objects are only pedestrians. But
true scenes may contain pedestrians, cars, and trucks, making
object detection more difficult. So in the future work, we will
make the virtual world more complex and similar to the real
scenes. Of course, the virtual world is useful not only for
pedestrian detection, but also for other vision problems, such
as segmentation, tracking, and pose estimation. We will do
more computer vision research in virtual scenes and make the
virtual world become a vision laboratory truly.
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TABLE I: AP Increment in each evaluation scene
TownCenter
voc
45.2%
30.8%

syn
79.2%
34.2%

Faster RCNN
DPM

incre
34%
3.6%

Atrium
voc
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45.4%

syn
67%
56.2%

Atrium PR Curve
1

1

0.9

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0.7

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0.6

FS(AP=67%)
FV(AP=47.5%)
DV(AP=45.4%)
DS(AP=56.2%)

0.5

Precision

FS(AP=79.2%)
FV(AP=45.2%)
DV(AP=30.8%)
DS(AP=34.2%)

0.5

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Recall

(a) Town Center

FS(AP=90.4%)
FV(AP=89.5%)
DV(AP=81%)
DS(AP=85.3%)

0.5

0.4

0

incre
0.9%
4.3%

PETS 2009 PR Curve

1

0

PETS 2009
voc
89.5%
81%

syn
90.4%
85.3%

0.9

Precision

Precision

TownCenter PR Curve

incre
12.5%
10.8%

0.5

0.6

0.7

0.8

0.9

1

0

0

0.1

Recall

(b) Atrium

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Recall

(c) PETS 2009

Fig. 5: Precision-recall curve with PASCAL VOC 2007 metric in different scenes.

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