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References

    1. 1)
      • 40. Zhang, B.: ‘Reliable classification of vehicle types based on cascade classifier ensembles’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (1), pp. 322332.
    2. 2)
      • 23. Sarfraz, M.S., Khan, M.H.: ‘A probabilistic framework for patch based vehicle type recognition’. VISAPP, 2011, pp. 358363.
    3. 3)
      • 17. Boonsim, N., Prakoonwit, S.: ‘Car make and model recognition under limited lighting conditions at night’, Pattern Anal. Appl., 2016, pp. 113, (doi:10.1007/s10044-016-0559-6).
    4. 4)
      • 29. Huttunen, H., Yancheshmeh, F.S., Chen, K.: ‘Car Type Recognition with Deep Neural Networks’, 2016, arXiv Prepr. arXiv1602.07125.
    5. 5)
      • 22. Llorca, D., Colas, D., Daza, I.: ‘Vehicle model recognition using geometry and appearance of car emblems from rear view images’. 17th IEEE Int. Conf. on Intelligent Transportation Systems, 2014, pp. 30943099.
    6. 6)
      • 32. Deng, J., Krause, J., Fei-fei, L.: ‘Fine-grained crowdsourcing for fine-grained recognition’. IEEE Conf. on Computer Vision and Pattern Recognition, 2013, pp. 580587.
    7. 7)
      • 36. The PASCAL Visual Object Classes’. Available at http://pascallin.ecs.soton.ac.uk/challenges/VOC/, accessed March 2015.
    8. 8)
      • 27. Ren, S., He, K., Girshick, R., et al: ‘Faster R-CNN: Towards real-time object detection with region proposal networks’. Advances in Neural Information Processing Systems, 2015, pp. 9199.
    9. 9)
      • 8. Andrews, S., Tsochantaridis, I., Hofmann, T.: ‘Support vector machines for multiple-instance learning’. Advances in Neural Information Processing Systems, 2002, pp. 561568.
    10. 10)
      • 35. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2005, pp. 886893.
    11. 11)
      • 21. Santos, D., Correia, P.L.: ‘Car recognition based on back lights and rear view features’. 10th Int. Workshop on Image Analysis for Multimedia Interactive Services, 2009, pp. 137140.
    12. 12)
      • 30. Gao, Y., Lee, H.: ‘Local tiled deep networks for recognition of vehicle make and model’, Sensors, 2016, 16, (2), p. 226.
    13. 13)
      • 4. Ambardekar, A., Nicolescu, M., Bebis, G., et al: ‘Vehicle classification framework: a comparative study’, EURASIP J. Image Video Process., 2014, 2014, (1), pp. 113.
    14. 14)
      • 25. Razavian, A.S., Azizpour, H., Sullivan, J., et al: ‘CNN features off-the-shelf: an astounding baseline for recognition’. IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2014, pp. 512519.
    15. 15)
      • 1. Sun, Z., George, B., Ronald, M.: ‘On-road vehicle detection: a review’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (5), pp. 694711.
    16. 16)
      • 7. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., et al: ‘Object detection with discriminatively trained part-based models.’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (9), pp. 16271645.
    17. 17)
      • 31. Zhang, Y., Wei, X.S., Wu, J., et al: ‘Weakly supervised fine-grained categorization with part-based image representation’, IEEE Trans. Image Process., 2016, 25, (4), pp. 17131725.
    18. 18)
      • 9. Platt, J.: ‘Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods’, Adv. Large Margin Classif., 1999, 10, (3), pp. 6174.
    19. 19)
      • 15. Petrovic, V., Cootes, T.: ‘Analysis of features for rigid structure vehicle type recognition’. British Machine Vision Conf., 2004, pp. 587596.
    20. 20)
      • 2. Al-Smadi, M., Abdulrahim, K., Salam, R.A.: ‘Traffic surveillance: a review of vision based vehicle detection, recognition and tracking’, Int. J. Appl. Eng. Res., 2016, 11, (1), pp. 713726.
    21. 21)
      • 20. Yang, H., Zhai, L., Liu, Z., et al: ‘An efficient method for vehicle model identification via logo recognition’. Int. Conf. on Computational and Information Sciences, 2013, pp. 10801083.
    22. 22)
      • 13. Pearce, G., Pears, N.: ‘Automatic make and model recognition from frontal images of cars’. 8th IEEE Int. Conf. on Advanced Video and Signal based Surveillance, 2011, pp. 373378.
    23. 23)
      • 11. Conos, M.: ‘Recognition of vehicle make from a frontal view’. Master Thesis, Czech Tech, 2007.
    24. 24)
      • 24. Herout, A., Fit, G.: ‘BoxCars: 3D boxes as CNN input for improved fine-grained vehicle recognition’. IEEE Conf. on Computer Vision and Pattern Recognition, 2016, pp. 30063015.
    25. 25)
      • 14. Saravi, S., Edirisinghe, E.a.: ‘Vehicle make and model recognition in CCTV footage’. 18th Int. Conf. on Digital Signal Processing, 2013, pp. 16.
    26. 26)
      • 28. Girshick, R., Iandola, F., Darrell, T., et al: ‘Deformable part models are convolutional neural networks’. IEEE Conf. on Computer Vision and Pattern Recognition, 2015, pp. 437446.
    27. 27)
      • 18. Hsieh, J., Chen, L.: ‘Vehicle make and model recognition using symmetrical SURF’. Advanced Video and Signal Based Surveillance, 2013, pp. 472477.
    28. 28)
      • 12. Clady, X., Negri, P., Milgram, M., et al: ‘Multi-class vehicle type recognition system’, Artificial Neural Networks in Pattern Recognition,(Lecture Notes in Computer Science), 2008, pp. 228239.
    29. 29)
      • 38. Chang, C., Lin, C.: ‘LIBSVM: a library for support vector machines’, ACM Trans. Intell. Syst. Technol., 2011, 2, (3), pp. 127.
    30. 30)
      • 26. Gao, Y., Lee, H.J.: ‘Vehicle make recognition based on convolutional neural network’. 2nd Int. Conf. on Information Science and Security, 2015, pp. 14.
    31. 31)
      • 37. NTOU-MMR Dataset’. Available at http://mmplab.cs.ntou.edu.tw/mmplab/MMR/MMR.html, accessed October 2016.
    32. 32)
      • 6. Yousaf, K., Iftikhar, A., Javed, A.: ‘Comparative analysis of automatic vehicle classification techniques: a survey’, Int. J. Image, Graph. Signal Process., 2012, 4, (9), p. 52.
    33. 33)
      • 19. Psyllos, A., Anagnostopoulos, C.N., Kayafas, E.: ‘Vehicle model recognition from frontal view image measurements’, Comput. Stand. Interfaces, 2011, 33, (2), pp. 142151.
    34. 34)
      • 3. Sun, Z., George, B., Ronald, M.: ‘On-road vehicle detection using optical sensors: a review’. 7th IEEE Int. Conf. on Intelligent Transportation Systems, 2004, pp. 585590.
    35. 35)
      • 33. Zhang, N., Farrell, R., Iandola, F., et al: ‘Deformable part descriptors for fine-grained recognition and attribute prediction’. EEE Int. Conf. on Computer Vision, 2013, pp. 729736.
    36. 36)
      • 16. Munroe, D.T., Madden, M.G.: ‘Multi-class and single-class classification approaches to vehicle model recognition from images’. 16th Irish Conf. on Artificial Intelligence and Cognitive Science, 2005, pp. 93102.
    37. 37)
      • 34. Fang, J., Zhou, Y., Yu, Y., et al: ‘Fine-grained vehicle model recognition using a coarse-to-fine convolutional neural network architecture’, IEEE Trans. Intell. Transp. Syst., 2016, pp. 111.
    38. 38)
      • 39. Hsieh, J.-W., Chen, L.-C., Chen, D.-Y.: ‘Symmetrical SURF and its applications to vehicle detection and vehicle make and model recognition’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (1), pp. 620.
    39. 39)
      • 5. Zhou, Y., Liu, L., Shao, L., et al: ‘DAVE: a unified framework for fast vehicle detection and annotation’, 2016, arXiv:1607.04564v2, pp. 116.
    40. 40)
      • 10. Yang, L., Luo, P., Loy, C.C., et al: ‘A large-scale car dataset for fine-grained categorization and verification’. IEEE Conf. on Computer Vision and Pattern Recognition, 2015, pp. 39733981.
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