Skip to main content

2014 | OriginalPaper | Buchkapitel

Low-Rank Matrix Recovery for Traffic Sign Recognition in Image Sequences

verfasst von : Deli Pei, Fuchun Sun, Huaping Liu

Erschienen in: Foundations and Practical Applications of Cognitive Systems and Information Processing

Verlag: Springer Berlin Heidelberg

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

We consider the problem of traffic sign recognition in image sequences. In many cases, image sequences of traffic signs can be collected from consecutive videos and these images have high correlation with each other. While traditional traffic sign recognition approaches focus on how to extract better features and design more powerful classifiers, most of these methods neglected this correlation. In this paper, we introduce the low-rank matrix recovery model to exploit the correlation among images with similar appearances to enhance feature representation. By recovering the underlying low-rank matrix from the original feature matrix consists of feature vectors of image sequences, we are able to attenuate the influence of corruption, such as noise and motion blur. Experiments are conducted on GTSRB dataset to evaluate our method, and noticeable performance gain is observed by using low-rank matrix recovered from original matrix. We obtain very impressive results on several super-class accuracy while get comparable performance with state-of-the-art results on global accuracy.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

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!

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!

Literatur
2.
Zurück zum Zitat Kardkovacs ZT, Paroczi Z, Varga E, Siegler A, Lucz P (2011) Real-time traffic sign recognition system. In: proceedings of second international conference on cognitive infocommunications(CogInfoCom), pp 1–5 Kardkovacs ZT, Paroczi Z, Varga E, Siegler A, Lucz P (2011) Real-time traffic sign recognition system. In: proceedings of second international conference on cognitive infocommunications(CogInfoCom), pp 1–5
3.
Zurück zum Zitat Maldonado-Bascon S, Lafuente-Arroyo S, Gil-Jimenez P, Gomez-Moreno H, Lopez-Ferreras F (2007) Road-sign detection and recognition based on support vector machines. IEEE Trans Intell Trans Syst 8(2):264–278CrossRef Maldonado-Bascon S, Lafuente-Arroyo S, Gil-Jimenez P, Gomez-Moreno H, Lopez-Ferreras F (2007) Road-sign detection and recognition based on support vector machines. IEEE Trans Intell Trans Syst 8(2):264–278CrossRef
4.
Zurück zum Zitat Ruta A, Li Y, Liu X (2010) Robust class similarity measure for traffic sign recognition. IEEE Trans Intell Trans Syst 11(4):846–855CrossRef Ruta A, Li Y, Liu X (2010) Robust class similarity measure for traffic sign recognition. IEEE Trans Intell Trans Syst 11(4):846–855CrossRef
5.
Zurück zum Zitat Stallkamp J, Schlipsing M, Salmen J, Igel C, Man versus computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw (in press) Stallkamp J, Schlipsing M, Salmen J, Igel C, Man versus computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw (in press)
6.
Zurück zum Zitat Ciresan D, Meier U, Mascim J, Schmidhuber J (2011) A committee of neural networks for traffic sign classification. In: Proceedings of international joint conference on neural networks (IJCNN), pp 1918–1921 Ciresan D, Meier U, Mascim J, Schmidhuber J (2011) A committee of neural networks for traffic sign classification. In: Proceedings of international joint conference on neural networks (IJCNN), pp 1918–1921
7.
Zurück zum Zitat Sermanet P, LeCun Y (2011) Traffic sign recognition with multi-scale convolutional networks. In: Proceedings of the international joint conferece on neural networks (IJCNN), pp 2809–2813 Sermanet P, LeCun Y (2011) Traffic sign recognition with multi-scale convolutional networks. In: Proceedings of the international joint conferece on neural networks (IJCNN), pp 2809–2813
8.
Zurück zum Zitat Zaklouta F, Stanciulescu B, Hamdoun O (2011) Traffic sign classification using k-d trees and random forests. In: Proceedings of the international joint conference on neural networks (IJCNN), pp 2151–2155 Zaklouta F, Stanciulescu B, Hamdoun O (2011) Traffic sign classification using k-d trees and random forests. In: Proceedings of the international joint conference on neural networks (IJCNN), pp 2151–2155
9.
Zurück zum Zitat Bahlmann C, Zhu Y, Ramesh V, Pellkofer M, Koehler T (2005) A system for traffic sign detection, tracking, and recognition using color, shape, and motion information. In: Proceedings of IEEE intelligent vehicles symposium, pp 255–260 Bahlmann C, Zhu Y, Ramesh V, Pellkofer M, Koehler T (2005) A system for traffic sign detection, tracking, and recognition using color, shape, and motion information. In: Proceedings of IEEE intelligent vehicles symposium, pp 255–260
10.
Zurück zum Zitat Ruta A, Li Y, Liu X (2008) Detection, tracking and recognition of traffic signs from video input. In: Proceedings of international IEEE conference on intelligent transportation systems, School of Information Systems, pp 55–60 Ruta A, Li Y, Liu X (2008) Detection, tracking and recognition of traffic signs from video input. In: Proceedings of international IEEE conference on intelligent transportation systems, School of Information Systems, pp 55–60
11.
Zurück zum Zitat Ruta A, Li Y, Liu X (2010) Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recognit 43(1):416–430CrossRefMATH Ruta A, Li Y, Liu X (2010) Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recognit 43(1):416–430CrossRefMATH
12.
Zurück zum Zitat Moutarde F, Bargeton A, Herbin A, Chanussot L (2007) Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular traffic signs recognition system. In: Proceedings of IEEE intelligent vehicles symposium, pp 1122–1126 Moutarde F, Bargeton A, Herbin A, Chanussot L (2007) Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular traffic signs recognition system. In: Proceedings of IEEE intelligent vehicles symposium, pp 1122–1126
13.
Zurück zum Zitat Lafuente-Arroyo S, Maldonado-Bascon S, Gil-Jimenez P, Acevedo-Rodriguez J, Lopez-Sastre RJ (2007) A tracking system for automated inventory of road signs. In: Proceedings of the IEEE intelligent vehicles symposium, pp 166–171 Lafuente-Arroyo S, Maldonado-Bascon S, Gil-Jimenez P, Acevedo-Rodriguez J, Lopez-Sastre RJ (2007) A tracking system for automated inventory of road signs. In: Proceedings of the IEEE intelligent vehicles symposium, pp 166–171
14.
Zurück zum Zitat Zhou X, Yang C, Yu W, Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans Pattern Anal Mach Intell (in press) Zhou X, Yang C, Yu W, Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans Pattern Anal Mach Intell (in press)
15.
Zurück zum Zitat Cui X, Huang J, Zhang S, Metaxas D (2012) Background subtraction using low rank and group sparsity constraints. In: Proceedings of the European conference on computer vision (ECCV) Cui X, Huang J, Zhang S, Metaxas D (2012) Background subtraction using low rank and group sparsity constraints. In: Proceedings of the European conference on computer vision (ECCV)
16.
Zurück zum Zitat Shen X, Wu Y (2012) A unified approach to salient object detection via low rank matrix recovery. In: Proceedings of the computer vision and pattern recognition (CVPR) Shen X, Wu Y (2012) A unified approach to salient object detection via low rank matrix recovery. In: Proceedings of the computer vision and pattern recognition (CVPR)
17.
Zurück zum Zitat Yan J, Zhu M, Liu H, Liu Y (2010) Visual saliency detection via sparsity pursuit. IEEE Signal Process Lett 17(8):739–742CrossRef Yan J, Zhu M, Liu H, Liu Y (2010) Visual saliency detection via sparsity pursuit. IEEE Signal Process Lett 17(8):739–742CrossRef
18.
Zurück zum Zitat Peng Y, Ganesh A, Wright J, Ma Y (2010) Robust alignment by sparse and low-rank decomposition for linearly correlated images. In: Proceedings of the computer vision and pattern recognition (CVPR) Peng Y, Ganesh A, Wright J, Ma Y (2010) Robust alignment by sparse and low-rank decomposition for linearly correlated images. In: Proceedings of the computer vision and pattern recognition (CVPR)
19.
Zurück zum Zitat Fazel M, Hindi H, Boyd SP (2001) A rank minimization heuristic with application to minimum order system approximation. In: Proceedings of the american control conference (ACC), pp 4734–4739 Fazel M, Hindi H, Boyd SP (2001) A rank minimization heuristic with application to minimum order system approximation. In: Proceedings of the american control conference (ACC), pp 4734–4739
20.
Zurück zum Zitat Candes E, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM 58:1–37 Candes E, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM 58:1–37
22.
Zurück zum Zitat Lin Z, Chen M, Ma Y (2009) The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Technical report, UIUC UILU-ENG-09-2215, arXiv:1009.5055 Lin Z, Chen M, Ma Y (2009) The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Technical report, UIUC UILU-ENG-09-2215, arXiv:1009.5055
23.
Zurück zum Zitat Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874 Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874
24.
Zurück zum Zitat Stallkamp J, Schlipsing M, Salmen J, Igel C (2011) The German traffic sign recognition benchmark: a multi-class classification competition. In: Proceedings of the international joint conference on neural networks (IJCNN), pp 1453–1460 Stallkamp J, Schlipsing M, Salmen J, Igel C (2011) The German traffic sign recognition benchmark: a multi-class classification competition. In: Proceedings of the international joint conference on neural networks (IJCNN), pp 1453–1460
25.
Zurück zum Zitat Ciresan DC, Meier U, Gambardella LM, Schmidhuber J (2010) Deep, big, simple neural nets for handwritten digit recognition. Neural Comput 22:3207–3220 Ciresan DC, Meier U, Gambardella LM, Schmidhuber J (2010) Deep, big, simple neural nets for handwritten digit recognition. Neural Comput 22:3207–3220
Metadaten
Titel
Low-Rank Matrix Recovery for Traffic Sign Recognition in Image Sequences
verfasst von
Deli Pei
Fuchun Sun
Huaping Liu
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-37835-5_74