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Volumn 19, Issue 9, 2018, Pages 2913-2922

Vehicle Type Recognition in Surveillance Images from Labeled Web-Nature Data Using Deep Transfer Learning

Author keywords

convolutional neural network; surveillance images; transfer learning; unsupervised domain adaptation; Vehicle type recognition

Indexed keywords

AUTOMOBILE MANUFACTURE; CONVOLUTION; IMAGE ENHANCEMENT; IMAGE RECOGNITION; IMAGING SYSTEMS; IMAGING TECHNIQUES; LEARNING SYSTEMS; MONITORING; NETWORK SECURITY; NEURAL NETWORKS; PERSONNEL TRAINING; SPACE SURVEILLANCE; VEHICLES;

EID: 85036536980     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2017.2765676     Document Type: Article
Times cited : (62)

References (30)
  • 1
    • 85027945147 scopus 로고    scopus 로고
    • Vehicle logo recognition system based on convolutional neural networks with a pretraining strategy
    • Aug.
    • Y. Huang, R. Wu, Y. Sun, W. Wang, and X. Ding, "Vehicle logo recognition system based on convolutional neural networks with a pretraining strategy," IEEE Trans. Intell. Transp. Syst., vol. 16, no. 4, pp. 1951-1960, Aug. 2015.
    • (2015) IEEE Trans. Intell. Transp. Syst. , vol.16 , Issue.4 , pp. 1951-1960
    • Huang, Y.1    Wu, R.2    Sun, Y.3    Wang, W.4    Ding, X.5
  • 2
    • 77953120605 scopus 로고    scopus 로고
    • Vehicle logo recognition using a SIFT-based enhanced matching scheme
    • Jun.
    • A. P. Psyllos, C.-N. E. Anagnostopoulos, and E. Kayafas, "Vehicle logo recognition using a SIFT-based enhanced matching scheme," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 2, pp. 322-328, Jun. 2010.
    • (2010) IEEE Trans. Intell. Transp. Syst. , vol.11 , Issue.2 , pp. 322-328
    • Psyllos, A.P.1    Anagnostopoulos, C.-N.E.2    Kayafas, E.3
  • 3
    • 84888873104 scopus 로고    scopus 로고
    • An integrative approach to accurate vehicle logo detection
    • Sep.
    • H. Pan and B. Zhang, "An integrative approach to accurate vehicle logo detection," J. Elect. Comput. Eng., vol. 2013, Sep. 2013, Art. no. 391652.
    • (2013) J. Elect. Comput. Eng. , vol.2013
    • Pan, H.1    Zhang, B.2
  • 5
    • 84959531264 scopus 로고    scopus 로고
    • Recognition of car makes and models from a single traffic-camera image
    • Dec.
    • H. He, Z. Shao, and J. Tan, "Recognition of car makes and models from a single traffic-camera image," IEEE Trans. Intell. Transp. Syst., vol. 16, no. 6, pp. 3182-3192, Dec. 2015.
    • (2015) IEEE Trans. Intell. Transp. Syst. , vol.16 , Issue.6 , pp. 3182-3192
    • He, H.1    Shao, Z.2    Tan, J.3
  • 6
    • 33745920531 scopus 로고    scopus 로고
    • Edge-based rich representation for vehicle classification
    • Oct.
    • X. Ma and W. E. L. Grimson, "Edge-based rich representation for vehicle classification," in Proc. Int. Conf. Comput. Vis., Oct. 2005, pp. 1185-1192.
    • (2005) Proc. Int. Conf. Comput. Vis. , pp. 1185-1192
    • Ma, X.1    Grimson, W.E.L.2
  • 7
    • 34047216475 scopus 로고    scopus 로고
    • Analysis of features for rigid structure vehicle type recognition
    • V. S. Petrovic and T. F. Cootes, "Analysis of features for rigid structure vehicle type recognition," in Proc. Brit. Mach. Vis. Conf., 2004, pp. 587-596.
    • (2004) Proc. Brit. Mach. Vis. Conf. , pp. 587-596
    • Petrovic, V.S.1    Cootes, T.F.2
  • 9
    • 72949120132 scopus 로고    scopus 로고
    • Vehicle type recognition based on harris corner detector
    • J. Li, W. Zhao, and H. Guo, "Vehicle type recognition based on harris corner detector," in Proc. 2nd Int. Conf. Transp. Eng., 2009, pp. 3320-3325.
    • (2009) Proc. 2nd Int. Conf. Transp. Eng. , pp. 3320-3325
    • Li, J.1    Zhao, W.2    Guo, H.3
  • 10
    • 84879296134 scopus 로고    scopus 로고
    • Reliable classification of vehicle types based on cascade classifier ensembles
    • Mar.
    • B. Zhang, "Reliable classification of vehicle types based on cascade classifier ensembles," IEEE Trans. Intell. Transp. Syst., vol. 14, no. 1, pp. 322-332, Mar. 2013.
    • (2013) IEEE Trans. Intell. Transp. Syst. , vol.14 , Issue.1 , pp. 322-332
    • Zhang, B.1
  • 11
    • 84865712685 scopus 로고    scopus 로고
    • Vehicle type and make recognition by combined features and rotation forest ensemble
    • B. Zhang and Y. Zhou, "Vehicle type and make recognition by combined features and rotation forest ensemble," Int. J. Pattern Recognit. Artif. Intell., vol. 26, no. 3, p. 1250004, 2012.
    • (2012) Int. J. Pattern Recognit. Artif. Intell. , vol.26 , Issue.3 , pp. 1250004
    • Zhang, B.1    Zhou, Y.2
  • 12
    • 34047213276 scopus 로고    scopus 로고
    • An oriented-contour point based voting algorithm for vehicle type classification
    • Aug.
    • P. Negri, X. Clady, M. Milgram, and R. Poulenard, "An oriented-contour point based voting algorithm for vehicle type classification," in Proc. Int. Conf. Pattern Recognit., Aug. 2006, pp. 574-577.
    • (2006) Proc. Int. Conf. Pattern Recognit. , pp. 574-577
    • Negri, P.1    Clady, X.2    Milgram, M.3    Poulenard, R.4
  • 13
    • 84923844219 scopus 로고    scopus 로고
    • Vehicle detection and recognition for intelligent traffic surveillance system
    • Y. Tang, C. Zhang, R. Gu, P. Li, and B. Yang, "Vehicle detection and recognition for intelligent traffic surveillance system," Multimedia Tools Appl., vol. 76, no. 4, pp. 5817-5832, 2017.
    • (2017) Multimedia Tools Appl. , vol.76 , Issue.4 , pp. 5817-5832
    • Tang, Y.1    Zhang, C.2    Gu, R.3    Li, P.4    Yang, B.5
  • 14
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Nov.
    • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
    • (1998) Proc. IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 15
    • 85028222906 scopus 로고    scopus 로고
    • Vehicle type classification using a semisupervised convolutional neural network
    • Aug.
    • Z. Dong, Y. Wu, M. Pei, and Y. Jia, "Vehicle type classification using a semisupervised convolutional neural network," IEEE Trans. Intell. Transp. Syst., vol. 16, no. 4, pp. 2247-2256, Aug. 2015.
    • (2015) IEEE Trans. Intell. Transp. Syst. , vol.16 , Issue.4 , pp. 2247-2256
    • Dong, Z.1    Wu, Y.2    Pei, M.3    Jia, Y.4
  • 16
    • 84959184327 scopus 로고    scopus 로고
    • A large-scale car dataset for fine-grained categorization and verification
    • Jun.
    • L. Yang, P. Luo, C. C. Loy, and X. Tang, "A large-scale car dataset for fine-grained categorization and verification," in Proc. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 3973-3981.
    • (2015) Proc. Comput. Vis. Pattern Recognit. , pp. 3973-3981
    • Yang, L.1    Luo, P.2    Loy, C.C.3    Tang, X.4
  • 17
    • 85028395788 scopus 로고    scopus 로고
    • Fine-grained vehicle model recognition using a coarse-to-fine convolutional neural network architecture
    • Jul.
    • J. Fang, Y. Zhou, Y. Yu, and S. Du, "Fine-grained vehicle model recognition using a coarse-to-fine convolutional neural network architecture," IEEE Trans. Intell. Transp. Syst., vol. 18, no. 7, pp. 1782-1792, Jul. 2017.
    • (2017) IEEE Trans. Intell. Transp. Syst. , vol.18 , Issue.7 , pp. 1782-1792
    • Fang, J.1    Zhou, Y.2    Yu, Y.3    Du, S.4
  • 19
    • 84986266809 scopus 로고    scopus 로고
    • BoxCars: 3D boxes as CNN input for improved fine-grained vehicle recognition
    • Jun.
    • J. Sochor, A. Herout, and J. Havel, "BoxCars: 3D boxes as CNN input for improved fine-grained vehicle recognition," in Proc. Comput. Vis. Pattern Recognit., Jun. 2016, pp. 3006-3015.
    • (2016) Proc. Comput. Vis. Pattern Recognit. , pp. 3006-3015
    • Sochor, J.1    Herout, A.2    Havel, J.3
  • 20
    • 85012926200 scopus 로고    scopus 로고
    • A model for fine-grained vehicle classification based on deep learning
    • Sep.
    • S. Yu, Y. Wu, W. Li, Z. Song, and W. Zeng, "A model for fine-grained vehicle classification based on deep learning," Neurocomputing, vol. 257, pp. 97-103, Sep. 2017.
    • (2017) Neurocomputing , vol.257 , pp. 97-103
    • Yu, S.1    Wu, Y.2    Li, W.3    Song, Z.4    Zeng, W.5
  • 21
    • 77956031473 scopus 로고    scopus 로고
    • A survey on transfer learning
    • Oct.
    • S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
    • (2010) IEEE Trans. Knowl. Data Eng. , vol.22 , Issue.10 , pp. 1345-1359
    • Pan, S.J.1    Yang, Q.2
  • 23
    • 84919819401 scopus 로고    scopus 로고
    • Maximum mean discrepancy for class ratio estimation: Convergence bounds and kernel selection
    • A. Iyer, S. Nath, and S. Sarawagi, "Maximum mean discrepancy for class ratio estimation: Convergence bounds and kernel selection," in Proc. Int. Conf. Mach. Learn. (ICML), 2014, pp. 530-538.
    • (2014) Proc. Int. Conf. Mach. Learn. (ICML) , pp. 530-538
    • Iyer, A.1    Nath, S.2    Sarawagi, S.3
  • 25
    • 84888856531 scopus 로고    scopus 로고
    • Equivalence of distance-based and RKHS-based statistics in hypothesis testing
    • D. Sejdinovic, B. Sriperumbudur, A. Gretton, and K. Fukumizu, "Equivalence of distance-based and RKHS-based statistics in hypothesis testing," Ann. Stat., vol. 41, no. 5, pp. 2263-2291, 2013.
    • (2013) Ann. Stat. , vol.41 , Issue.5 , pp. 2263-2291
    • Sejdinovic, D.1    Sriperumbudur, B.2    Gretton, A.3    Fukumizu, K.4
  • 28
    • 84969962996 scopus 로고    scopus 로고
    • Deep convolutional neural networks for computeraided detection: CNN architectures, dataset characteristics and transfer learning
    • May
    • H.-C. Shin et al., "Deep convolutional neural networks for computeraided detection: CNN architectures, dataset characteristics and transfer learning," IEEE Trans. Med. Imag., vol. 35, no. 5, pp. 1285-1298, May 2016.
    • (2016) IEEE Trans. Med. Imag. , vol.35 , Issue.5 , pp. 1285-1298
    • Shin, H.-C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.