Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Introduction Read chapter Read first chapter

2017 | OriginalPaper | Chapter

2. Roadside Video Data Analysis Framework

Authors : Brijesh Verma, Ligang Zhang, David Stockwell

Published in: Roadside Video Data Analysis

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This chapter introduces a general framework for roadside video data analysis. The main processing steps in the framework are described separately.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Introduction
next chapter Non-deep Learning Techniques for Roadside Video Data Analysis
Literature
1.
W. Maddern, A. Stewart, C. McManus, B. Upcroft, W. Churchill et al., Illumination invariant imaging: applications in robust vision-based localisation, mapping and classification for autonomous vehicles, in Proceedings of the Visual Place Recognition in Changing Environments Workshop, IEEE International Conference on Robotics and Automation (ICRA), 2014
2.
D.V. Nguyen, L. Kuhnert, K.D. Kuhnert, Structure overview of vegetation detection. A novel approach for efficient vegetation detection using an active lighting system. Robot. Auton. Syst. 60, 498–508 (2012)CrossRef
3.
M.P. Ponti, Segmentation of low-cost remote sensing images combining vegetation indices and mean shift. IEEE Geosci. Remote Sens. Lett. 10, 67–70 (2013)CrossRef
4.
M.R. Blas, M. Agrawal, A. Sundaresan, K. Konolige, Fast color/texture segmentation for outdoor robots, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2008, pp. 4078–4085
5.
B. Zafarifar, P.H.N. de With, Grass field detection for TV picture quality enhancement, in International Conference on Consumer Electronics (ICCE), Digest of Technical Papers, 2008, pp. 1–2
6.
I. Harbas, M. Subasic, Motion estimation aided detection of roadside vegetation, in 7th International Congress on Image and Signal Processing (CISP), 2014, pp. 420–425
7.
I. Harbas, M. Subasic, Detection of roadside vegetation using features from the visible spectrum, in 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014, pp. 1204–1209
8.
D.V. Nguyen, L. Kuhnert, K.D. Kuhnert, Spreading algorithm for efficient vegetation detection in cluttered outdoor environments. Robot. Auton. Syst. 60, 1498–1507 (2012)CrossRef
9.
D.V. Nguyen, L. Kuhnert, T. Jiang, S. Thamke, K.D. Kuhnert, Vegetation detection for outdoor automobile guidance, in IEEE International Conference on Industrial Technology (ICIT), 2011, pp. 358–364
10.
A. Schepelmann, R.E. Hudson, F.L. Merat, R.D. Quinn, Visual segmentation of lawn grass for a mobile robotic lawnmower, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010, pp. 734–739
11.
V. Balali, M. Golparvar-Fard, Segmentation and recognition of roadway assets from car-mounted camera video streams using a scalable non-parametric image parsing method. Autom. Constr. 49(Part A), 27–39 (2015)
12.
N.W. Campbell, B.T. Thomas, T. Troscianko, Automatic segmentation and classification of outdoor images using neural networks. Int. J. Neural Syst. 8, 137–144 (1997)CrossRef
13.
D.V. Nguyen, L. Kuhnert, S. Thamke, J. Schlemper, K.D. Kuhnert, A novel approach for a double-check of passable vegetation detection in autonomous ground vehicles, in 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2012, pp. 230–236
14.
S. Chowdhury, B. Verma, D. Stockwell, A novel texture feature based multiple classifier technique for roadside vegetation classification. Exp. Syst. Appl. 42, 5047–5055 (2015)CrossRef
15.
A. Bosch, X. Muñoz, J. Freixenet, Segmentation and description of natural outdoor scenes. Image Vis. Comput. 25, 727–740 (2007)CrossRef
16.
W. Guo, U.K. Rage, S. Ninomiya, Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model. Comput. Electron. Agric. 96, 58–66 (2013)CrossRef
17.
F. Ahmed, H.A. Al-Mamun, A.S.M.H. Bari, E. Hossain, P. Kwan, Classification of crops and weeds from digital images: a support vector machine approach. Crop Prot. 40, 98–104 (2012)CrossRef
18.
D.-X. Liu, T. Wu, B. Dai, Fusing ladar and color image for detection grass off-road scenario, in IEEE International Conference on Vehicular Electronics and Safety (ICVES), 2007, pp. 1–4
19.
L. Zhang, B. Verma, D. Stockwell, Spatial contextual superpixel model for natural roadside vegetation classification. Pattern Recogn. 60, 444–457 (2016)CrossRef
20.
D.M. Bradley, R. Unnikrishnan, J. Bagnell, Vegetation detection for driving in complex environments, in IEEE International Conference on Robotics and Automation, 2007, pp. 503–508
21.
K.M. Wurm, R. Kummerle, C. Stachniss, W. Burgard, Improving robot navigation in structured outdoor environments by identifying vegetation from laser data, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009, pp. 1217–1222
22.
K.M. Wurm, H. Kretzschmar, R. Kümmerle, C. Stachniss, W. Burgard, Identifying vegetation from laser data in structured outdoor environments. Robot. Auton. Syst. 62, 675–684 (2014)CrossRef
23.
Y. Kang, K. Yamaguchi, T. Naito, Y. Ninomiya, Multiband image segmentation and object recognition for understanding road scenes. IEEE Trans. Intell. Transp. Syst. 12, 1423–1433 (2011)CrossRef
24.
L. Lu, C. Ordonez, E.G. Collins Jr., E.M. DuPont, Terrain surface classification for autonomous ground vehicles using a 2D laser stripe-based structured light sensor, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009, pp. 2174–2181
25.
D.-V. Nguyen, L. Kuhnert, T. Jiang, S. Thamke, K.-D. Kuhnert, Vegetation detection for outdoor automobile guidance, in IEEE International Conference on Industrial Technology (ICIT), 2011, pp. 358–364
26.
P. Felzenszwalb, D. Huttenlocher, Efficient graph-based image segmentation. Int. J. Comput. Vis. 59, 167–181 (2004)CrossRef
27.
J. Shotton, J. Winn, C. Rother, A. Criminisi, Textonboost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int. J. Comput. Vis. 81, 2–23 (2009)CrossRef
28.
S. Gould, R. Fulton, D. Koller, Decomposing a scene into geometric and semantically consistent regions, in IEEE 12th International Conference on Computer Vision (ICCV), 2009, pp. 1–8
29.
C. Farabet, C. Couprie, L. Najman, Y. LeCun, Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915–1929 (2013)CrossRef
30.
A. Sharma, O. Tuzel, D.W. Jacobs, Deep hierarchical parsing for semantic segmentation, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 530–538
31.
B. Fulkerson, A. Vedaldi, S. Soatto, Class segmentation and object localization with superpixel neighborhoods, in IEEE 12th International Conference on Computer Vision (ICCV), 2009, pp. 670–677
32.
S. Gould, J. Rodgers, D. Cohen, G. Elidan, D. Koller, Multi-class segmentation with relative location prior. Int. J. Comput. Vis. 80, 300–316 (2008)CrossRef
33.
J. Tighe, S. Lazebnik, Superparsing: scalable nonparametric image parsing with superpixels, in European Conference on Computer Vision (ECCV), 2010, pp. 352–365
34.
D. Munoz, J.A. Bagnell, M. Hebert, Stacked hierarchical labeling, in European Conference on Computer Vision (ECCV), 2010, pp. 57–70
35.
R. Socher, C.C. Lin, C. Manning, A.Y. Ng, Parsing natural scenes and natural language with recursive neural networks, in Proceedings of the 28th International Conference on Machine Learning (ICML), 2011, pp. 129–136
36.
B. Micusik, J. Kosecka, Semantic segmentation of street scenes by superpixel co-occurrence and 3D geometry, in IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), 2009, pp. 625–632
37.
L. Zhang, B. Verma, D. Stockwell, S. Chowdhury, Spatially constrained location prior for scene parsing, in International Joint Conference on Neural Networks (IJCNN), 2016, pp. 1480–1486
38.
Y. Jimei, B. Price, S. Cohen, Y. Ming-Hsuan, Context driven scene parsing with attention to rare classes, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 3294–3301
39.
A. Singhal, L. Jiebo, Z. Weiyu, Probabilistic spatial context models for scene content understanding, in IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), 2003, pp. 235–241
40.
D. Batra, R. Sukthankar, C. Tsuhan, Learning class-specific affinities for image labelling, in IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), 2008, pp. 1–8
41.
Z. Lei, J. Qiang, Image segmentation with a unified graphical model. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1406–1425 (2010)CrossRef
42.
R. Xiaofeng, B. Liefeng, D. Fox, RGB-(D) scene labeling: features and algorithms, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2759–2766
43.
V. Lempitsky, A. Vedaldi, A. Zisserman, Pylon model for semantic segmentation, in Advances in Neural Information Processing Systems, 2011, pp. 1485–1493
44.
F. Tung, J.J. Little, Scene parsing by nonparametric label transfer of content-adaptive windows. Comput. Vis. Image Underst. 143, 191–200 (2016)CrossRef
45.
46.
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su et al., Conditional random fields as recurrent neural networks. arXiv preprint arXiv:​1502.​03240 (2015)
47.
48.
Metadata
Title
Roadside Video Data Analysis Framework
Authors
Brijesh Verma
Ligang Zhang
David Stockwell
Copyright Year
2017
Publisher
Springer Singapore
Book
Roadside Video Data Analysis

Print ISBN: 978-981-10-4538-7

Electronic ISBN: 978-981-10-4539-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-4539-4_2