Computer Vision Applications: Third Workshop, WCVA 2018, Held in Conjunction with ICVGIP 2018, Hyderabad, India, December 18, 2018, Revised Selected Papers

138 Pages·2019·19.347 MB·English

by  Chetan Arora, Kaushik Mitra

Chetan Arora
Kaushik Mitra (Eds.)
Communications in Computer and Information Science 1019
Computer Vision
Applications
Third Workshop, WCVA 2018
Held in Conjunction with ICVGIP 2018
Hyderabad, India, December 18, 2018
Revised Selected Papers
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Chetan Arora Kaushik Mitra (Eds.)
(cid:129)
Computer Vision
Applications
Third Workshop, WCVA 2018
Held in Conjunction with ICVGIP 2018
Hyderabad, India, December 18, 2018
Revised Selected Papers
123
Editors
ChetanArora Kaushik Mitra
Department ofComputer Science Department ofElectrical Engineering
andEngineering Indian Institute of Technology Madras
Indian Institute of Technology Delhi Chennai, India
NewDelhi, India
ISSN 1865-0929 ISSN 1865-0937 (electronic)
Communications in Computer andInformation Science
ISBN 978-981-15-1386-2 ISBN978-981-15-1387-9 (eBook)
https://doi.org/10.1007/978-981-15-1387-9
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Preface
It is our pleasure to present the proceedings of the Third Workshop on Computer
VisionApplications(WCVA2018).Theworkshopwascolocatedwiththe11thIndian
Conference on Computer Vision, Graphics and Image Processing (ICVGIP), at the
International Institute of Information Technology (IIIT) Hyderabad. Keeping in line
with the traditions established from the past workshops, WCVA 2018 provided an
appropriate platform for academic researchers as well as industry and government
research labs to present and discuss their ideas on novel techniques for various
computer vision applications.
The proceedings consists of 10 papers. All papers had an oral presentation during
theworkshopmeetingonDecember18,2018.WethankthegeneralchairsProf.Rama
Chelappa and Prof. Santanu Chaudury for their mentorship and all the reviewers for
theircarefulandtimelyreview.Therewere32paperssubmittedwhichweredistributed
to a technical program committee comprised of 31 well-qualified reviewers. All the
papers were reviewed by 3 or more reviewers.
The WCVA program also included an inaugural talk by Prof. Santanu Chaudhury,
Director IIT Jodhpur, and a keynote talk by Prof. Saket Anand from IIIT Delhi. The
keynote talk covered an important area and was titled “Computer vision for Wildlife:
fromConservationtoConflictManagement.”Afterwards,therewereoralpresentations
of all the selected papers. We hope that the workshop papers capture the most
importantworksbeingcarriedoutbytheIndianaswellastheinternationalresearchers
in important application areas of computer vision.
Finally, we thank the organizers of ICVGIP 2018 for providing the local logistic
arrangementandadministrativeassistance,whichwasessentialfortheexecutionofthe
workshop. We would also like to thank all the authors for submitting their original
work and all the participants for their interest and support.
December 2018 Chetan Arora
Kaushik Mitra
Organization
General Chairs
Rama Chellappa University of Maryland, College Park, USA
Santanu Chaudhury IIT Jodhpur, India
Program Chairs
Chetan Arora IIT Delhi, India
Kaushik Mitra IIT Madras, India
Technical Program Committee
Saket Anand IIIT Delhi, India
Abir Das IIT Kharagpur, India
Aditya Nigam IIT Mandi, India
Gaurav Sharma NEC Labs, USA
Anubha Gupta IIIT Delhi, India
Ravikiran Sarvadevabhatla IIIT Hyderabad, India
Prithwijit Guha IIT Guwahati, India
Shanmuganathan Raman IIT Gandhinagar, India
Suresh Sundaram IIT Guwahati, India
Manoj Sharma Rice University, USA
Jun-Cheng Chen University of Maryland, College Park, USA
Vijay Rengarajan CMU, USA
Rushil Anirudh Lawrence Livermore National Laboratory, USA
Kuldeep Kulkarni CMU, USA
Arnab Bhattacharya IIT Kanpur, India
Sunil Simon IIT Kanpur, India
Amay Karkare IIT Kanpur, India
Vinay Namboodri IIT Kanpur, India
Sumantra Dutta Roy IIT Delhi, India
Gaurav Harit IIT Jodhpur, India
Ayesha Chaudhury JNU, India
Vikram Goyal IIIT Delhi, India
Vineeth Balasubramanian IIT Hyderabad, India
Anup Namboodri IIIT Hyderabad, India
C. V. Jawahar IIIT Hyderabad, India
Vineet Gandhi IIIT Hyderabad, India
Parag Chaudhury IIT Bombay, India
Uma Mudenagudi BVB CET, Hubli, India
Soma Biswas IISc, Bangalore, India
viii Organization
Venkatesh Babu IISc, Bangalore, India
Parag Singla IIT Delhi, India
Mansi Sharma IIT Madras, India
Contents
Depth Augmented Semantic Segmentation Networks
for Automated Driving. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Hazem Rashed, Senthil Yogamani, Ahmad El-Sallab, Arindam Das,
and Mohamed El-Helw
Optic Disc Segmentation in Fundus Images Using Anatomical Atlases
with Nonrigid Registration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Ambika Sharma, Monika Aggarwal, Sumantra Dutta Roy, Vivek Gupta,
Praveen Vashist, and Talvir Sidhu
Bird Species Classification Using Transfer Learning
with Multistage Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Akash Kumar and Sourya Dipta Das
A Deep Learning Paradigm for Automated Face Attendance. . . . . . . . . . . . . 39
Rahul Kumar Gupta, Shreeja Lakhlani, Zahabiya Khedawala,
Vishal Chudasama, and Kishor P. Upla
Robust Detection of Iris Region Using an Adapted SSD Framework. . . . . . . 51
Saksham Jain and Indu Sreedevi
Dynamic Image Networks for Human Fall Detection
in 360-degree Videos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Sumeet Saurav, T. N. D. Madhu Kiran, B. Sravan Kumar Reddy,
K. Sanjay Srivastav, Sanjay Singh, and Ravi Saini
Image Segmentation and Geometric Feature Based Approach for Fast
Video Summarization of Surveillance Videos. . . . . . . . . . . . . . . . . . . . . . . 79
Raju Dhanakshirur Rohan, Zeba ara Patel, Smita C. Yadavannavar,
C. Sujata, and Uma Mudengudi
Supervised Hashing for Retrieval of Multimodal Biometric Data. . . . . . . . . . 89
T. A. Sumesh, Vinay Namboodiri, and Phalguni Gupta
Pose Estimation for Distracted Driver Detection Using Deep
Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
Siddhesh Thakur, Bhakti Baheti, Suhas Gajre, and Sanjay Talbar
AECNN: Autoencoder with Convolutional Neural Network
for Hyperspectral Image Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Heena Patel and Kishor P. Upla
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Depth Augmented Semantic
Segmentation Networks
for Automated Driving
B
Hazem Rashed1, Senthil Yogamani2( ), Ahmad El-Sallab1, Arindam Das3,
and Mohamed El-Helw4
1 CDV AI Research, Cairo, Egypt
2 Valeo Vision Systems, Valeo, Cairo, Egypt
3 Detection Vision Systems, Valeo, Tuam, Ireland
{hazem.rashed,senthil.yogamani,
ahmad.el-sallab,arindam.das}@valeo.com
4 Nile University, Cairo, Egypt
[email protected]
Abstract. In this paper, we explore the augmentation of depth maps
to improve the performance of semantic segmentation motivated by the
geometric structure in automotive scenes. Typically depth is already
computed in an automotive system to localize objects and path plan-
ningandthuscanbeleveragedforsemanticsegmentation.Weconstruct
two networks that serve as a baseline for comparison which are “RGB
only” and “Depth only”, and we investigate the impact of fusion of
both cues using another two networks which are “RGBD concat”, and
“TwoStreamRGB+D”.Weevaluatethesenetworksontwoautomotive
datasets namely Virtual KITTI using synthetic depth and Cityscapes
using a standard stereo depth estimation algorithm. Additionally, we
evaluate our approach using monoDepth unsupervised estimator [10].
Two-stream architecture achieves the best results with an improvement
of5.7%IoUinVirtualKITTIand1%IoUinCityscapes.Thereisalarge
improvementforcertainclassesliketrucks,building,vanandcarswhich
haveanincreaseof29%,11%,9%and8%respectivelyinVirtualKITTI.
Surprisingly,CNNmodelisabletoproducegoodsemanticsegmentation
from depth images only. The proposed network runs at 4fps on TitanX
GPU, Maxwell architecture.
· ·
Keywords: Semantic segmentation Visual perception Automated
driving
1 Introduction
Recently,semanticsegmentationhasgainedahugeattentioninthefieldofcom-
puter vision. One of the main applications is autonomous driving where the
car is able to understand the environment by providing a class for each pixel
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C.AroraandK.Mitra(Eds.):WCVA2018,CCIS1019,pp.1–13,2019.
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