Proceedings of the 2016 IEEE/SICE International Symposium on
System Integration, Sapporo Convention Center, Sapporo, Japan,
December 13-15, 2016

TuP2E.3

Automatic Houseware Registration System for
Informationally-Structured Environment
Kazuto Nakashima1 , Julien Girard2 , Yumi Iwashita3 and Ryo Kurazume4
Abstract— To provide daily-life assistance appropriately by aservice robot, the management of houseware’s information ina room or a house is an indispensable function. Especially,
the information about what and where objects are in theenvironment are fundamental and critical knowledge. We cantrack housewares with high reliability by attaching markers
such as RFID tags to them, however, markerless housewares
management system is still useful since it is easy-to-use andlow cost. In this work, we present an object management
system using an egocentric vision and a region-based convolutional neural network (R-CNN) to automatically detect andregister housewares. The proposed system consists of smart
glasses equipped with a wearable camera, a cloud databasewhich manages object information, and a processing server for
detecting and registering housewares to the cloud database.
We perform two experiments. First, we train the R-CNN on
a newly-constructed dataset to detect various housewares andconfigure a houseware-specific detector. All systems are composed of ROS packages. Second, we conduct experiments forautomatic housewares registration using the proposed system.
We demonstrate that the proposed system can detect, recognize,
and register housewares approximately in real time.

I. I NTRODUCTION
In recent years, the rapid aging of the population has
caused serious problems such as a labor shortage in hospitals
or care facilities. To mitigate this problem, the developmentof a service robot coexisting with a human in a daily-life
environment is an urgent challenge. We have been focusing
on the concept of a context-aware intelligent space, which
is so-called Informationally-Structured Environment (ISE),
and developing its architecture named ROS-TMS[1][2]. The
ISE is an intelligent space where various sensors are distributed and combined, organizing a sensor network within
the environment. According to the main idea of the ISE,
the ROS-TMS not only utilizes the sensors mounted on the
robot but also embeds the various sensors such as laser range
finder or cameras in the environment. Thus, the service robot
can obtain information of an extensive scope and carry out
various tasks without being limited by own performance,
by using all the sensors and resources the ISE provides.
1 Kazuto Nakashima is with the Graduate School of Information Scienceand Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku,
Fukuoka, Japan. k nakashima@irvs.ait.kyushu-u.ac.jp
2 Julien
Girard
is
with
the
ENSTA
ParisTech,
1024,
Boulevard des Marechaux, 91762 Palaiseau Cedex, France.

julien.girard@ensta-paristech.fr
3 Yumi
California

Iwashita is with the Jet Propulsion Laboratory,
Institute of Technology, Pasadena, CA 91109, USA.

Yumi.Iwashita@jpl.nasa.gov
4 Ryo Kurazume is with the Faculty of Information Science and ElectricalEngineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, Japan.

kurazume@ait.kyushu-u.ac.jp
978-1-5090-3328-7/16/$31.00 ©2016 IEEE

Furthermore, it models an integrated system modularizing
all of functions and processes. Executing the modularizedprocesses and interprocess communication is based on the
Robot Operating System (ROS)[3] framework.
In the field of developing service robots, the most prospective service is a fetch-and-carry task, which is comparatively
easy to design. To achieve it, a service robot needs toknow where the target object is or which state it is. It isalso necessary to measure position and status of objects
within the symbiosis space on a regular basis. Current ROSTMS manages houseware information by attaching some
identifiable markers such as an RFID tag on them, as we
describe in Section II-A. This approach is taken in the
research fields of ubiquitous computing and the Internet ofThings (IoT). Although these systems are able to reliably
track the objects, they may not completely recognize thestatus of the environment if the sensors can only measureparticular objects, if its measurement range is limited, or if a
new object is added. One solution to make the measurementof scattered housewares more flexible is to use the techniqueof markerless object tracking. However, it still requires highresolution images and high-precision object detection technique. Nakayama et al.[4] developed a smart goggles system
which recognizes objects appearing in the daily-life scene inreal-time and enables to improve a visual memory. Owing
to the goggles equipped with a camera, rich informationof the object that the user is gazing and has interest canbe obtained from close position. Their object recognition
technique is based on a hand-designed image feature. In
recent years, an effectiveness of the feature automaticallylearned from massive volumes of data has been shown inthe various field of real-world perception, which is based on
the well-known machine learning technique Deep Learning.
Furthermore, that can not only process in real-time on theparallel computing environment, but also can be useful toextend the scale of object recognition and improve theruntime performance.
In this paper, we propose an automatic houseware registration system using smart glasses and the latest object detection
technique based on the Deep Learning, which is introducedin Section III.
Our contributions can be summarized as follows:• We train the houseware-specific detector based on thelatest object detection technique Faster R-CNN[5] andmodularize it with ROS messaging framework.
• We develop an automatic houseware registration system using smart glasses, our cloud database, and thehouseware-specific object detection technique.
337

B. Problems in managing housewares(a) Intelligent Cabinet System
Fig. 1.

•(b) Motion Capture System

Conventional systems to manage housewares in ROS-TMS

We perform an empirical evaluation and demonstratethat our system can detect housewares and register theirinformation in real time even without attaching any
identifiable markers on them.

II. I NFORMATIONALLY-S TRUCTURED E NVIRONMENT
A. ROS-TMS
As mentioned in the previous section, we have been developing an integrated service robot system for InformationallyStructured Environment, named ROS-TMS. Currently, it hasover 150 modules useful for a service robot to provide
daily-life assistance. Those modules include user interface,task scheduler, robot controller, movement path planner,and a database which manages ambient information. In thissection, we firstly explain about existing systems to measure
housewares in the environment and a cloud database to
manage their information. Secondly, we address the current
issues in terms of managing housewares.
1) Existing houseware measurement systems: In current
ROS-TMS, there are several systems to measure housewares
in a room. Figure 1 (a) shows an exterior of one of thesystems, Intelligent Cabinet System (ICS). In the system,an RFID reader and load cells are installed on its storage
area and measure the RFID tags attached to stored objects
to recognize individually and estimate the positions by analyzing the weight distribution on multiple load cells. Besides,
our refrigerator, named Intelligent Refrigerator System (IRS),
also has a similar type of this mechanism. Figure 1 (b) shows
motion capture camera, Vicon Bonita. In our experimentspace, over 20 cameras are embedded to track position andposture of objects by measuring reflective markers attachedon them. To recognize an individual object, it needs to haveat least three markers on it and register those structure modelin advance.
2) Cloud database: We manage the housewares in theenvironment with the cloud database. Our cloud databaseis composed of a database (MongoDB) and an interface toread/write data through the wireless network. As well as theambient information that the embedded sensors capture, itmanages every information relating to the symbiosis space
including static/dynamic maps and the task information thatthe robot needs for operation. As for the houseware, its
position and name are provided in the database.

In current systems, there are some limitations to manage
housewares. First of all, although an RFID-based measurement system such as the ICS can reliably track a stored
item, its measurement has several constraints. Housewares
should be located within the bounded measurement areaof the RFID reader and tags should be attached to them
manually. Meanwhile, a motion capture has a wide range ofmeasurement, however, it requires some laborious processes
to attach identifiable features on the object to be recognized
as well.
For these reasons, we focus on a generic object detection technique to enable markerless tracking that uses
the appearance of the item itself. In current ROS-TMS,
various cameras are distributed to the human, the robot, andthe environment, thus it is possible to detect housewares
from various images of the daily life scene and manage
them in a wider scope. However, among these cameras,
embedded camera has difficulty to detect small-sized items
such as housewares, because of the low resolution and themeasurement distance. In this project, we take particularnote of an egocentric vision from the wearable camera
where it is possible to obtain the houseware image withcomparably high resolution and comprehend the interestsof the inhabitant. In the following section, we introduce anotable system that manages housewares automatically byapplying generic object detection to the egocentric vision.
III. AUTOMATIC H OUSEWARE R EGISTRATION S YSTEM
A. System overview
The proposed system consists of three parts as illustrated
in Figure 2:• Smart glasses to stream the inhabitant’s egocentric
vision and enable the interactive services.
• Processing server to detect housewares from thestreamed image and register their information to thecloud database.
• Cloud database to manage the information of an inhabitant, a robot, and housewares in the ROS-TMS.
The procedure of automatic registration is as follows.
At first, the smart glasses the inhabitant wears capture animage by its embedded camera and stream them continuously
to the network as egocentric vision. The processing server
receives them to detect housewares. The detection function isbased on the neural network-based object detection techniqueas we describe in Section III-D. The detected housewares
are registered to the existing ROS-TMS database with theircategory name. Thus the system can automatically manage
scattered housewares in the environment.
B. Houseware detection from the egocentric vision
Egocentric vision, also known as first-person vision, canbe employed as a clue to recognize human actions or activities, estimate their intentions, and understand the life patterns
since the continuously-acquirable vision contains the richinformation about interactions with objects in a daily life.
338

Fig. 3. Smartglasses: EPSON Moverio BT-200. The left pane is the exterior.
We attached some reflective markers on it to track the position and theposture. The right pane is a manually-reconstructed scene for illustrative
purpose.
Fig. 2.

System overview

Egocentric vision can indeed capture contextual informationand head motions related to the use of a particular object.
There are several works to solve such tasks by using object
recognition/detection techniques on egocentric vision. In theactivity recognition task, for instance, Pirsiavash et al.[6]focused on observable objects to encode an egocentric video
into a representative feature. They employed Deformable Part
Model[7] approach for detecting objects. Faith et al.[8] usedregions of hands and objects which can be observed in thevision as well, and are recently improved with convolutional
neural network (CNN) frameworks[9]. Meanwhile, as for an
application, Huang et al.[10] proposed an egocentric interaction system based on the hand/fingertip detection and theyutilized the latest object detection model, Faster R-CNN[5].
Owing to the model, they achieved real-time accurate handdetection.
As described before, we focus on the fetch-and-carry task
as the most promising service for daily life assistance. Regarding the task, the measurement of housewares which havea possibility to be required by the inhabitant is important to
let the robot know their positions promptly. Thus we focus
on egocentric vision to tackle the problem. Owing to the factthat the housewares which the inhabitant has interests tend
to appear in egocentric vision, high immediacy of the service
provision can be expected by detecting objects from thevision and updating our database. Furthermore, a wearable
camera can obtain higher-resolution images from the position
close to the object than an embedded camera fixed in walls
or ceilings.
In recent years, several high-performance wearable cameras are provided in the market and that enables us tocapture egocentric vision easily just only by attaching itto the head. Especially, smart glasses are very popular asa human-computer interaction device since it has not only
onboard cameras but microphones, speakers, and see-throughdisplays. Besides, it operates on a lightweight OS, so that
we can use it as a portable computing device. Thus we
use the smart glasses as a egocentric camera, supposing theapplications of a user interface in the future.
C. Object detection techniques based on Deep Learning
Since the ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2012 that Krizhevsky et al.[11] won withdeep neural networks named AlexNet, convolutional neuralnetworks (CNNs) have gained a great deal of attention as
a powerful method to achieve a high accuracy of the image
understanding. As the latest result, CNNs have surpassed human’s ability to identify images, with achieving an extremely
low error rate[12][13].
In addition to image-based classification, the acquired
knowledge can be applied to a generic object detection task,
taking an approach to input regions of object proposal in
an image into a CNN. In the architecture called regionbased convolutional neural network (R-CNN)[14], it detects
multiple object proposals from an image in advance by applying the segmentation method called Selective Search[15]and predicts a semantic label of the region-of-interest (RoI)by propagating it through a CNN, so that achieves high accuracy object detection with a multiclass classification. Morerecently, there are some works to speed up the detection
time. Fast R-CNN[16] introduced RoI pooling layer which
can pool any region in the final convolutional layer whichcorresponds to object proposal into the fixed-size feature
map, so that enables to detect multiple objects by a one-shot
propagation. Moreover, Faster R-CNN[5] replaced Selective
Search that was a bottleneck at runtime with a neural network
called Region Proposal Networks (RPN). Now thus, with ahardware acceleration by a graphics processing unit (GPU),
it has become available to detect multiple objects withhigh accuracy approximately in real time. In the proposedsystem, we employ the latest architecture Faster R-CNN asa powerful function to understand what object humans areseeing in the daily life scene.
D. Houseware-specific detection network
In the original implementation of the Faster R-CNN[17],some models which are pre-trained on a large-scale dataset
are opened to the public and available to utilize while theircategories to detect differ from our situation; some of thedefault categories such as an animal or a vehicle in the
PASCAL VOC dataset[18] are unlikely to appear in theroom. We train the network with a newly created dataset
which contains appropriate classes such as a bottle, a book,and other housewares. Subsequently, we explain about our
settings of the network.
1) Network models: Faster R-CNN is composed of twoneural network-based modules. One is Region Proposal Network (RPN) that are based on a CNN and produces a set of339

TABLE I
T HE HOUSEWARE - SPECIFIC DATASET CONTAINING 2659 IMAGES OF 11
HOUSEWARE CATEGORIES . W ORD N ET ID IS IDENTIFIABLE NUMBER
MANAGED IN THE I MAGE N ET. # REPRESENTS “ NUMBER OF ”.

Category

WordNet ID# regions# imagesBook
Coffee canController
Cup
Dish
Glass
KettleTeapotWater bottle
Watering pot
Wine bottle

n02870526
n03062985
n03096960
n03147509
n03206908
n03438257
n03612814
n04398044
n04557648
n04560292
n04591713

133
213
200
169
128
179
205
599
478
176
556

116
179
179
153
110
149
183
579
437
153
421

3036

2659

Total

Fig. 4.

The architecture of Faster R-CNN[5]object proposals. The other is the Fast R-CNN detector[16]that are also based on a CNN. It classifies an object in theregion proposed by the RPN and simultaneously regresses
the region of the detected object (bounding box). Bothmodules have homogeneous convolutional layers to extract afull-image feature and share their parameters, thus the Faster
R-CNN models a unified network as shown in Figure 4. As
the sharable convolutional features, we use the ZF model[19]which has 5 convolutional layers and the VGG-16[20] model
which has 13 convolutional layers, which are investigatedin the paper[5] as the “fast” model and the “deep” model
respectively. We compare these models in terms of accuracy
and detection speed in runtime.
2) Dataset: We constructed a new dataset consisting of 11
housewares categories, which is a subset of the well-known
large-scale image dataset ImageNet[21] providing boundingboxes for thousands of object categories as ground truth. The
detail of our dataset is indicated in the Table I. Among themassive available images, we chose the categories with thefollowing two criteria.
••

It already exists in our experiment environment.
The bounding boxes are available as ground truth.

3) Training strategy: Three methods are discussed in thepaper[5] to train both RPN and Fast R-CNN, sharing theirconvolutional layers. We employ the approximate joint training method which makes a time-consuming evaluation easier.
This method is not rigorous for ignoring a part of the network
response preferred to be treated, however, it works effectively
with reducing the training time and keeping the accuracy.
Firstly, the sharable convolutional layers are initialized witha model pre-trained on the ImageNet classification set and theentire networks are simultaneously trained on the ImageNet
detection set as one network, minimizing the combined loss
from both RPN and Fast R-CNN.

E. Implementation details
1) Modularization by ROS framework: The ROS-TMS
employs Robot Operating System (ROS)[3] as an interprocess communication framework. ROS is efficient to configurea large distributed computing system. Furthermore, it iscritical for CNNs to utilize GPU acceleration for speedingup the runtime, hence the process possibly occupies muchof the available computing resources. In the future of ROSTMS, launching the multiple object detection processes foreach service is inefficient and can be a high load. For thesereasons, we modularized the Faster R-CNN operation withROS messaging functions and made the function sharable toimprove a functional reusability and decentralize the load on
the large-scale processing. Object detection is running on adecentralized server and can be used by other applications, orin any other generic research purpose. Basically, we extend
the original implementation[17] by Python launguage.
2) Android application on the smart glasses: Including
ours, most of the smart glasses are an Android-based device.
In order to stream the egocentric images into the local
network, we build an Android application running on themand even here employ the ROS messaging for packets of theimage.
IV. E XPERIMENTS
A. Evaluation of the houseware-specific Faster R-CNN
As described, we evaluate both the ZF model and the
VGG-16 model on our houseware dataset. In general, CNNs
require much of image resources to optimize their parameters. In this experiment, due to the shortage of our dataset,
we evaluate the performance of the houseware-specific Faster
R-CNN by 4-folds cross validation. At first, we divide theentire dataset into four subsets and validation is repeated fourtimes with different combinations of a training set and a test
set. For each fold, one of the subsets is used as a test set
and the other three subsets form a training set. When training
on this set we use 70k mini-batch iterations. The evaluation
metric is mean Average Precision (mAP) in accordance with340

Fig. 5. Example of houseware detection on a web camera. Four housewares
are bounded with red boxes and given the classified labels

the PASCAL VOC benchmark[18]. Predicted bounding box
is counted as a correct detection when the intersection areawith its ground truth exceed 50%. Thus the average accuracy
across all folds is computed as a final result. The evaluations
are all performed on the NVIDIA GTX Titan X GPU.
1) Performance of the detection: In Table II we show theevaluation results for both VGG16 model and ZF model. The
VGG-16 model achieves the higher result for all categories
with an mAP of 72.4% and the ZF model has an mAP of
66.3%. Although the way of evaluation is different fromthe original metric, we can say that the training properly
works, by taking into account the fact that the public result
on PASCAL VOC 2012 is 70.4% mAP[5]. The “book” and“glass” categories show the low accuracy comparing with theothers. One of the reasons is the remarkable changes in object
appearance according to the status (e.g., an open or closedbook) and the transparency of materials. Therefore we think
the accuracy can be improved by increasing the number oftraining samples. In training for each fold, the VGG-16 andthe ZF took 9.6 hours and 4.5 hours respectively. Figure 5
shows an example of a detection result.
B. Automatic registration experiments
Next, we conduct an experiment to automatically register
the detected housewares to the ROS-TMS database, with thetwo models trained in the Experiment A. In this experiment,
we confirm the following functions.
• Our system captures the egocentric vision from smart
glasses and stream them to the processing server.
• Our system detects multiple housewares within thegiven image and register the predicted result to the
database.
We observe a success rate of registration and its processing speed on the setting of that the smart glasses detecthousewares over consecutive frames. As for the details, we
set the smart glasses and multiple housewares in advanceas the glass can capture all of housewares. Subsequently,
our system starts to detect and register housewares from theFig. 6.

The setting of Experiment B described in Section IV-B

images captured by the smart glasses. Figure 6 shows thesettings of this experiment.
1) Results: As a result, our proposed system could registerall of the detected housewares. However, image quality isdeteriorated by the movement of the smart glasses and theaccuracy of object detection gets worse, for example, if theuser changes the face direction quickly. With the VGG-16
model, the proposed system achieved the detection in 0.131
sec and its frame rate is 7.5 fps. Meanwhile, with the ZF
model, the system achieved the detection in 0.064 sec andits frame rate is 8.8 fps. The entire processing time of the
VGG-16 model is slower than the ZF model, however, theprocess is operated in a sufficiently high speed for real-timeregistration. Figure 7 shows some examples that the proposedsystem detected on the houseware dataset.
V. C ONCLUSIONS
We developed the automatic houseware registration system using the latest object detection architecture and smart
glasses. We conducted the two experiments. In the first,
using the public large-scale image set, we trained housewarespecific Faster R-CNN and modularized it as a sharable
houseware detection server. In the second, we carried out theregistration experiment with the entire system. From thoseresults, we confirmed that our system can detect housewares
within the smart glasses’ vision and automatically register
their information to the database in real time.
The current system has not adopted the houseware identification on the instance level. Furthermore, it manages
only the information about a category. Our future works
include implementations of two functions. One is to estimate
a position of a detected houseware using the posture taken by
the position tracker in Figure 3 (a). The other is to identify
instances of housewares using the position information or
their appearance, that enables the service robot to find an
intended houseware from among scattered objects promptly.
ACKNOWLEDGEMENT
This research is supported by The Japan Science andTechnology Agency (JST) through its“Center of Innovation
341

TABLE IIR ESULTS IN 4- FOLDS CROSS VALIDATION . M AP DENOTES MEAN AVERAGE PRECISION .

Model

mAP

Book

Coffee canController

Cup

Dish

Glass

KettleTeapotWater bottle

Watering pot

Wine bottle

VGG-16
ZF

71.7
66.3

50.7
45.2

89.9
89.6

80.5
77.6

64.4
58.8

67.4
58.7

53.4
46.0

67.8
61.9

88.8
85.6

74.8
67.6

67.4
59.8

83.3
78.9

(a) Detection of the book
Fig. 7.

(a) Detection of the water bottle

Example of screens on the smart glasses which render the result of the housewares detection.

Science and Technology based Radical Innovation and Entrepreneurship Program (COI Program).”
R EFERENCES[1] Y. Pyo, K. Nakashima, S. Kuwahata, R. Kurazume, T. Tsuji, K. Morooka, and T. Hasegawa, “Service robot system with an informationallystructured environment,” Robotics and Autonomous Systems, vol. 74,
pp. 148–165, 2015.
[2] https://github.com/irvs/ros tms.
[3] M. Quigley, K. Conley, B. P. Gerkey, J. Faust, T. Foote, J. Leibs,
R. Wheeler, and A. Y. Ng, “ROS: an open-source robot operating
system,” in ICRA Workshop on Open Source Software, 2009.
[4] H. Nakayama, T. Harada, and Y. Kuniyoshi, “AI goggles: Real-time
description and retrieval in the real world with online learning,” inComputer and Robot Vision, 2009. CRV ’09. Canadian Conference
on, May 2009, pp. 184–191.
[5] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards
real-time object detection with region proposal networks,” CoRR, vol.
abs/1506.01497, 2015. [Online]. Available: http://arxiv.org/abs/1506.
01497[6] H. Pirsiavash and D. Ramanan, “Detecting activities of daily living infirst-person camera views,” in Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, 2012, pp. 2847–2854.
[7] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan,“Object detection with discriminatively trained part-based models,”IEEE transactions on pattern analysis and machine intelligence,
vol. 32, no. 9, pp. 1627–1645, 2010.
[8] A. Fathi, A. Farhadi, and J. M. Rehg, “Understanding egocentric
activities,” in International Conference on Computer Vision. IEEE,
2011, pp. 407–414.
[9] Y. Zhou, B. Ni, R. Hong, X. Yang, and Q. Tian, “Cascaded interactional targeting network for egocentric video analysis,” in The IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), June
2016.
[10] Y. Huang, X. Liu, X. Zhang, and L. Jin, “A pointing gesture based
egocentric interaction system: Dataset, approach and application,” in[10] Y. Huang, X. Liu, X. Zhang, and L. Jin, “A pointing gesture based
egocentric interaction system: Dataset, approach and application,” inProceedings of the IEEE Conference on Computer Vision and Pattern
Recognition Workshops, 2016, pp. 16–23.
[11] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification
with deep convolutional neural networks,” in Advances in neural
information processing systems, 2012, pp. 1097–1105.
[12] K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers:
Surpassing human-level performance on imagenet classification,” inProceedings of the IEEE International Conference on Computer
Vision, 2015, pp. 1026–1034.
[13] ——, “Deep residual learning for image recognition,” arXiv preprint
arXiv:1512.03385, 2015.
[14] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature
hierarchies for accurate object detection and semantic segmentation,”in Proceedings of the IEEE conference on computer vision and patternrecognition, 2014, pp. 580–587.
[15] J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders,“Selective search for object recognition,” International journal ofcomputer vision, vol. 104, no. 2, pp. 154–171, 2013.
[16] R. Girshick, “Fast R-CNN,” in Proceedings of the IEEE International
Conference on Computer Vision, 2015, pp. 1440–1448.
[17] https://github.com/rbgirshick/py-faster-rcnn.
[18] M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn,
and A. Zisserman, “The pascal visual object classes challenge: A
retrospective,” International Journal of Computer Vision, vol. 111,
no. 1, pp. 98–136, 2015.
[19] M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European Conference on Computer Vision.
Springer, 2014, pp. 818–833.
[20] K. Simonyan and A. Zisserman, “Very deep convolutional networks
for large-scale image recognition,” arXiv preprint arXiv:1409.1556,
2014.
[21] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei,“Imagenet: A large-scale hierarchical image database,” in Computer
Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference
on. IEEE, 2009, pp. 248–255.

342