Journal of Software Engineering and Applications, 2013, 6, 17-21
doi:10.4236/jsea.2013.67B004 Published Online July 2013 (http://www.scirp.org/journal/jsea)
Development of a Client-Server System for 3D Scene
Change Detection
Haoming Wang, Baowei Lin, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Koji Ichii
Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Japan.
Received April, 2013.
ABSTRACT
In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on
the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system
in tablets captures query images and sent them to the server to estimate the change area. In order to find region of
change, an existing change detection method has been applied into our system. Then the server sends detection result
image back to mobile device and visualize it. The result of system test shows that the system could detect change cor-
rectly.
Keywords: 3D Change Detection; Client-Server System; Tablet
1. Introduction
Recent surveillance technology developments motivate a
regular observation of places such as coast, slopes and
highways, where disasters and accidents happened with
high probability. At coast, a lot of wave dissipating
blocks are placed to decrease the power of waves. If the
configuration of the blocks is altered due to erosion, the
blocks might be collapsed and the waves would not be
dissipated. Hence, any small changes of the face must be
alerted. So a 3D scene change detection system will be
helpful to defend disasters and accidents.
In addition, people need a practical application to ob-
serve 3D scene change. Since a variety of mobile devices
are much cheaper and more powerful than before, a
3D scene change detection system on a mobile platform
is possible to be implemented. We present a system that
is capable of detecting 3D scene change based on query
image provided by a mobile platform. Image acquisition
is performed on a mobile device, and only selected key
frames are sent to a powerful server for change detection.
Then the detection results are returned to the mobile
device. We embedded an existing change detection
method into our system, finally we built a client-server
system which are easy to be used for 3D scene change
detection.
The rest of the paper is organized as follows. Related
work on registration is reviewed in section 2. In section 3,
we first describe the change detection algorithm briefly,
and then discuss about basic concept in our system. The
system implementation test is given in section 4.
2. Related Work
There are some researches focus on 3D change detection
because it has become easy to obtain 3D point clouds by
using a range finder or 3D reconstruction techniques
[1,7,8].
3D change detection methods with a laser range finder
have been proposed for environment monitoring and ro-
bot navigation. Goncaluves et al. [3] proposed a method
to visualized 3D displacement map of two 3D la-
ser-scanned scene point clouds. A method proposed by
Pollard et al. [4] updates probabilistic voxel models by
using a new image and models changes by a probability
of seeing observed pixel color. This method can deal
with any images with arbitrary (but known) cameras,
however, it strongly relays on GMM color models and
hence not adequate to objects with similar colors.
Taneja et al. [5] proposed a method for computing 2D
inconsistency maps combined with MRF and project
color difference of images back to 3D surfaces for 3D
scene change detection. The irrelevant changes such as
walking people, cars and vegetation are excluded by
classifying those changes. This method uses color infor-
mation of images which is not stable for illumination
changes.
Lin et al. [2,6] proposed an approach which is similar
to the work of Taneja et al. [5] to utilize 2D images with
3D scene geometry. Their key con tribution is to use lo cal
feature descriptors for detecting changes instead of color
information, because those are well known to be robust
to illumination changes, and invariant to many transfor-

Development of a Client-Server System for 3D Scene Change Detection 18
mations including rotation, translation, and scaling.
Our system chooses Lin’s algorithm as our image
processing algorithm on the server side, the details will
be introduced in next section.
3. System Overview
3.1. Change Detection Algorithm
We use Lin’s method to detection change in the server
side. In their algorithm, temporal changes are detected by
using 3D scene geometry at time A (reference) and the
image of the same scene at time B (query). To quickly
detect changes and visualize them for operators at a
regular observation, the method uses 3D-2D matching
between a 3D reference scene and 2D query images fol-
lowed by 2D-2D feature comparison between 2D training
and query images, which is superior to either of 3D-3D
or 2D-2D matching.
In the case of 3D-3D matching [3,9,10], two 3D
scenes are registered for change detection. This may be
able to provide a detail comparison between reference
and query 3D scenes, however, this approach is not suit-
able because 1) online and real-time 3D reconstruction is
very difficult and still a challenging problem, and 2) a 3D
laser range finder is not easy to handle at the places of
our task because of its heavy weight and the need of
electric power. In the case of 2D-2D matching [11-13],
images (or videos) taken by a fixed camera are compared
to detect changes, or more commonly, moving objects.
This approach is also not appropriate for the detection
purpose: a number of fixed cameras are needed to cover
the whole scene of the observed place, hence, it is im-
practical if in particular the scene is wide and large. If the
scene is observed by a hand-held camera for taking a
number of 2D images, the problem above may not arise.
However, reference and query 2D images are very diffi-
cult to register unless they have a large overlapping area
in each pair of the images.
In contrast, Lin’s approach uses the combination of
3D-3D and 2D-2D matching. Assume that 3D scene
geometry is given but only at time A as a reference 3D
scene, hence no 3D range finder is necessary. At time B
(query), a hand-held camera is used to take images of the
same scene, and the reference 3D geometry is used for
3D-2D matching for camera pose estimation. This en-
ables us to perform a robust 3D-2D matching.
3.2. Model Overview
In order to develop a client-server 3D scene change de-
tection system, we choose an android based tablet device
as a client for image acquisition and result visualization
in our system. We implement a client application which
can communicate with a server through network connec-
tion. Here we give an example of how a user uses our
client-server system. The user takes a tablet device to a
coast where he wants to do change detection, then, take
photos of the wave dissipatin g blocks. Th e photo s will be
sent to the server for change detection calculation. Then
the detected change points coordinates’ information will
be returned back to the client and the client application
draw these points on the query photos. User can immedi-
ately observe the changed area. Figure 1 shows the gen-
eral modules o f ou r syst em.
3.3. Combination with Change Detection
Algorithm
In our development, we want to combine Lin’s change
detection algorithm into our detection system. So, to
make the embedded algorithm low computational cost
and good matching integration is very important. Be-
cause the client system and the server system are sepa-
rated to each other, the connection which we considered
about is just via image because of follows:
 Change detection algorithm is only in charge of proc-
essing image.
 The server system is in charge of communication with
the clients. It only receives query images and sends
result images.
The basic concept of the interface design is shown in
Figure 2. Change detection program monitors query im-
age folder, and server program monitors result image
folder. When server receive a query image from clients,
it save this image to query image folder, this action trig-
gers the change detection program to read this image and
do image processing task, then it saves the result image
to result image folder, also this action triggers server
program to read the result image and send it to the cli-
ents.
Figure 3 shows the processing flow of our system.
First, when a query image is received from a client.
Server program saves this image into the query image
folder. At the same time, change detection program is
Figure 1. Module overview of the client-server system.

Development of a Client-Server System for 3D Scene Change Detection 19
Figure 2. Concept map of interface design.
Figure 3. Processing flow of combined systems.
monitoring the query image folder, detecting and saving
the result to the result folder. In this processing flow,
both server program and change detection program just
need to implement a file monitor module for integrating,
which is loosely coupled.
4. System Implementation
4.1. System Environment
On server side, we used Dell Latitude E6520 as our
server computer, the configuration information of the
laptop are as follows:
 Operation System: Fedora 16
 Processor: Intel(R) Core i7-2760QM CPU @ 2.40GH
 Memory (RAM): 8GB
On the client side, we used REGZA Tablet AT700 and
SONY XPERIA SGPT12 as the client tablets. REGZA
Tablet AT700 is based on Android 3.2.1 and SONY
XPERIA SGPT12 is based on Android 4.0.3. The reason
we used two tablets is that we want to test the perform-
ance of our system under different version of Android
platform. Also we tried to use them to test the multi-cli-

Development of a Client-Server System for 3D Scene Change Detection 20
ent supporting ability of our server program. We used
Wi-Fi as the network environment. The test implementa-
tion details are show n in next sect i on.
4.2. Implementation
4.2.1. Server Implementation
Before 3D change detection, we need to prepare the 3D
model dataset of time A. The point cloud of small artifi-
cial blocks was generated by 3D reconstruction with
Bundler [1] followed by Patch-based Multi-view Stereo
(PMVS2) [7,8]. Figure 4 shows one example image for
3D reconstruction. The 3D point cloud is shown in Fig-
ure 5.
Then we ran the server program of our system to wait
for images sent from clients. Once the system received
the images, the change detection program was imple-
mented, and then sent the detection result back to clients.
4.2.2. Client Implementation
After the preparation of server side, we ran a client, and
the main interface is shown in Figure 6. In this main
interface, the left side is the photo preview panel; the
right side is the directory list of result images of other
clients. Here we named each client by their device serial
number. Users can check the results of other clients from
different folders. After we pressed the capture button, an
image of the same scene as what prepared in sever side
was captured, and sent to the server waiting for the
change detection result. Once there was detection result
back to client, it was visualized on the right panel of the
main interface as shown in Figure 7.
Figure 4. An image for 3D reconstruction.
Figure 5. Reconstructed 3D point clouds of small bloc ks.
Figure 6. Main interface of client for capturing query image.

Development of a Client-Server System for 3D Scene Change Detection 21
Figure 7. Main interface of client for visualizing change detection result.
On the result image, red points specify an area that
change happened. Comparing to Figure 4, it is obvious
that the red point area is absolutely the change area (a
block removed). Although there are some noises on the
result image because of the limitation of the change de-
tection algorithm, the result is reasonable acceptable.
5. Conclusions
In this paper, a client-server system for 3D change detec-
tion has been introduced and preliminary test has been
implemented. In our future work, we will accelerate the
implementation speed, improve the detection accurate
and make better UI design.
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