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Vehicle detection in intelligent transport system under a hazy environment: a survey

Vehicle detection in intelligent transport system under a hazy environment: a survey

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Developing an intelligent transportation system has attracted a lot of attention in the recent past. Moreover, with the growing number of vehicles on the road most nations are adopting an intelligent transport system (ITS) for handling issues like traffic flow density, queue length, the average speed of the traffic, and total vehicles passing through a point in a specific time interval and so on. ITS by capturing traffic images and videos through cameras, helps the traffic control centres in monitoring and managing the traffic. Efficient and unfailing vehicle detection is a crucial step for the ITS. This study reviews different techniques and applications used around the world for vehicle detection under various environmental conditions based on video processing systems. This study also discusses the types of cameras used for vehicle detections, and the classification of vehicles for traffic monitoring and controlling. This study finally highlights the problems encountered during surveillance under extreme weather conditions.

References

    1. 1)
      • 67. Goodfellow, I., Bengio, Y., Courville, A.: ‘Deep learning’ (MIT Press, Cambridge, Mass, USA, 2016).
    2. 2)
      • 31. Zhang, J., Marszalek, M., Lazebnik, S., et al: ‘Local features and kernels for classification of texture and object categories: a comprehensive study’, Int. J. Comput. Vis., 2007, 73, (2), pp. 213238.
    3. 3)
      • 33. Siyal, M.Y.: ‘A neural vision-based approach for intelligent transportation system’, IEEE ICIT’ 02, Bankok, Thailand, 2002, pp. 456460.
    4. 4)
      • 24. Long, J., Shelhamer, E., Darrell, T.: ‘Fully convolutional networks for semantic segmentation’. Proc. IEEE Conf. Computer Vision Pattern Recognition, Boston, USA, 2015, pp. 34313440.
    5. 5)
      • 23. Bertozzi, M., Broggi, A., Cellario, M.: ‘Artificial vision in road vehicles’, Proc. IEEE, 2002, 90, (7), pp. 12581271.
    6. 6)
      • 30. Liu, Y., Tian, B., Chen, S., et al: ‘A survey of vision-based vehicle detection and tracking techniques in ITS’. Proc. 2013 IEEE Int. Conf. on Vehicular Electronics and Safety, Dongguan, China, 2013, pp. 7277.
    7. 7)
      • 17. Sussman, J.M.: ‘Perspectives on intelligent transportation systems’, 2005.
    8. 8)
      • 94. Lin, T. Y., Maire, M., Belongie, S., et al: ‘Microsoft COCO: common objects in context’, ECCV, 1, 4, 5, 7, 2014.
    9. 9)
      • 52. Cucchiara, R., Grana, C., Prati, A., et al: ‘Probabilistic classification for human behaviour analysis in transactions on systems’, Man Cybern., 2005, 35, pp. 4254.
    10. 10)
      • 18. Gupte, S., Masoud, O., Martin, R.F.K., et al: ‘Detection and classification of vehicles’, IEEE Trans. Intell. Transp. Syst., 2002, 3, (1), pp. 3747.
    11. 11)
      • 106. Huang, S.C., Chen, B.H., Cheng, Y.J.: ‘An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (5), pp. 23212332.
    12. 12)
      • 21. Dziech, W., Baran, R., Wiraszka, D.: ‘Signal compression based on zonal selection methods’. Proc. of the Int. Conf. of Mathematical Methods in Electromagnetic Theory, Kharkov, Ukraine, 2000, pp. 224226.
    13. 13)
      • 48. Barron, J., Fleet, D., Beauchemin, S.: ‘Performance of optical flow techniques’, Int. J. Comput. Vis., 1994, 12, (1), pp. 4277.
    14. 14)
      • 55. Zivkovic, Z.: ‘Improved adaptive Gaussian mixture model for background subtraction’. Int. Conf. on Pattern Recognition, Cambridge, UK, 2004.
    15. 15)
      • 61. Bouwmans, T., Zahzah, E.: ‘Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance’, Computer Vision and Image Understanding, 2014, 122, pp. 2234.
    16. 16)
      • 38. Iwasaki, Y., Kurogi, Y.: ‘Real-time robust vehicle detection through the same algorithm both day and night’. Proc. of the Int. Conf. on Wavelet Analysis and Pattern Recognition, Beijing, China, 2007, pp. 10081014.
    17. 17)
      • 63. Wang, Y., K., Chen, S.: ‘A robust vehicle detection approach’. IEEE Conf. on Advanced Video and Signal Based Surveillance, Tehran, Iran, 2005, pp. 117122.
    18. 18)
      • 16. Dickmanns, E.D.: ‘The development of machine vision for road vehicles in the last decade’, IEEE Intell. Veh. Symp. Proc., 2003, 1, pp. 268281.
    19. 19)
      • 97. Brostow, G. J., Fauqueur, J., Cipolla, R.: ‘Semantic object classes in video: A high-definition ground truth database’, Pattern Recognit. Lett., 2009, 30, (2), pp. 8897.
    20. 20)
      • 84. Socher, R., Bengio, Y., Manning, C.: ‘Deep learning for NLP (without magic)’. Tutorial Abstracts of ACL, Atlanta, USA, 2012, pp. 55.
    21. 21)
      • 81. Sutskever, I., Martens, J., Hinton, G.: ‘Generating text with recurrent neural networks’. Proc. of the 28th Int. Conf. on Machine Learning (ICML 11), Bellevue, USA, 2011, pp. 10171024.
    22. 22)
      • 2. Zhang, J., Wang, F.Y., Wang, K., et al: ‘Data-driven intelligent transportation systems: a survey’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (4), pp. 16241639.
    23. 23)
      • 35. Cucchiara, R., Grana, C., Piccardi, M., et al: ‘Improving shadow suppression in moving object detection with HSV color information’. IEEE Proc. Int. Conf. on Intelligent Transportation Systems, Oakland, USA, 2001, pp. 334339.
    24. 24)
      • 51. Heikkila, J., Silven, O.: ‘A real-time system for monitoring of cyclists and pedestrians’. Proc. of 2nd IEEE Workshop on Visual Surveillance, Fort Collins, USA, 1999, pp. 7481.
    25. 25)
      • 1. Buch, N., Velastin, S.A., Orwell, J.: ‘A review of computer vision techniques for the analysis of urban traffic’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (3), pp. 920939.
    26. 26)
      • 32. Bouwmans, T., Gonzalez, J., Shan, C., et al: ‘Special issue on background modelling for foreground detection in real-world dynamic scenes’, Mach. Vis. Appl., 2014, 25, (5), pp. 11011103.
    27. 27)
      • 77. Dosovitskiy, A., Fischer, P., Srringenberg, J.T., et al: ‘Discriminative unsupervised feature learning with convolutional neural networks’, CoRR, 2014, vol. abs/1406, no. 6909.
    28. 28)
      • 5. Wang, G., Xiao, D., Gu, J.: ‘Review on vehicle detection based on video for traffic surveillance’, IEEE Int. Conf. Automation and Logistics, Qingdao, China, September 2008, pp. 29612966.
    29. 29)
      • 20. Dule, E., Gokmen, M., Beratoglu, M.S.: ‘A convenient feature vector construction for vehicle color recognition’. Proc. Int. Conf. Neural Network, WSEAS, Lasi, Romania, 2010, pp. 250255.
    30. 30)
      • 78. Kavukcuoglu, K., Sermanet, P., Boureau, Y., et al: ‘Learning convolutional feature hierarchies for visual recognition’, Adv. Neural. Inf. Process. Syst., 2010, 23, pp. 10901098.
    31. 31)
      • 65. Yin, M., Zhang, H., Meng, H., et al: ‘An HMM based algorithm for vehicle detection in congested traffic situation’. IEEE Intelligent Transportation Systems Conf., Seattle, USA, 2007, pp. 736741.
    32. 32)
      • 71. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’. Proc. of Advances in Neural Information Processing Systems, Las Vegas, USA, 2012, pp. 10971105.
    33. 33)
      • 90. Sermanet, P, Eigen, D, Zhang, X, et al: ‘Overfeat: integrated recognition, localization and detection using convolutional networks’. Advances in Neural Information Processing Systems [S.1]: ICLR Press, Banff, Canada, 2014, pp. 10551061.
    34. 34)
      • 95. Geiger, A., Lenz, P., Urtasun, R.: ‘Are We ready for autonomous driving?’. The KITTI Vision Benchmark Suite, Conf. on Computer Vision and Pattern Recognition (CVPR), Providence, USA, 2012.
    35. 35)
      • 92. Cordts, M., Omran, M., Ramos, S., et al: ‘The cityscapes dataset’. CVPR Workshop on the Future of Datasets in Vision, Las Vegas, USA, 2015.
    36. 36)
      • 76. He, K., Zhang, X., Ren, S., et al: ‘Deep residual learning for image recognition’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016, pp. 770778.
    37. 37)
      • 87. Luckow, A., Cook, M., Ashcraft, N., et al: ‘Deep learning in the automotive industry: applications and tools’, CoRR, 2017, vol. abs/1705.00346.
    38. 38)
      • 19. Janowski, L., Kozłowski, P., Baran, R., et al: ‘Quality assessment for a visual and automatic license plate recognition’, Multimedia Tools Appl., 2014, 68, (1), pp. 2340.
    39. 39)
      • 45. Cutler, R., Davis, L.S.: ‘Model-based object tracking in monocular image sequences of road traffic scenes’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (8), pp. 781796.
    40. 40)
      • 59. Jenifa, R., A., T, , Akila, C., et al: ‘Rapid background subtraction from video sequence’. IEEE Int. Conf. on Computing, Electronic and Electrical Technologies (ICCEET), Kumaracoil, India, 2012, pp. 10771086.
    41. 41)
      • 107. Huang, S.C., Chen, B.H., Wang, W.J.: ‘Visibility restoration of single hazy images captured in real-world weather conditions’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (10), pp. 18141824.
    42. 42)
      • 6. Chen, Z., Ellis, T., Velastin, S.A.: ‘Vehicle type categorization: a comparison of classification schemes’. IEEE Conf. Intelligent Transportation Systems Proc., ITSC, Washington, DC, USA, 2011, pp. 7479.
    43. 43)
      • 11. Bishop, R.: ‘Intelligent vehicle applications worldwide’, IEEE Trans. Intell. Transp. Syst., 2000, 15, (1), pp. 7881.
    44. 44)
      • 110. Li, S., Ren, W., Zhang, J., et al: ‘Fast single image rain removal via a deep decomposition-composition network’, Comput. Vis. Pattern Recognit. (CVPR), 2018, 186, pp. 4857.
    45. 45)
      • 100. Mithun, N.C., Rashid, N.U., Rahman, S.M.: ‘Detection and classification of vehicles from video using multiple time-spatial images’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (3), pp. 12151225.
    46. 46)
      • 58. Bouwmans, T., El-Baf, F., Vachon, B.: ‘Background modelling using mixture of gaussians for foreground detection: a survey’, Recent Patents Comput. Sci., 2008, 1, (3), pp. 219237.
    47. 47)
      • 27. Rai, M., Husain, A.A., Maity, T., et al: ‘Advance intelligent video surveillance system (AIVSS): a future aspect’ (In Video Surveillance IntechOpen, London, UK, 2018).
    48. 48)
      • 91. Russakovsky, O., Deng, J., Su, H., et al: ‘Imagenet large scale visual recognition challenge’, IJCV, 2015, 115, (3), pp. 211252.
    49. 49)
      • 69. Wang, Z.: ‘The applications of deep learning on traffic identification’, 2016. Available at https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf.
    50. 50)
      • 85. Zhou, Y., Nejati, H., Do, T.T., et al: ‘Image-based vehicle analysis using deep neural network: A systematic study’. IEEE Int. Conf. on Digital Signal Processing, Beijing, China, 2016.
    51. 51)
      • 70. Liu, W., Zhang, M., Cai, Y.: ‘An ensemble deep learning method for vehicle type classification on visual traffic surveillance sensors’, IEEE Access, 2017, 5, pp. 2441724425.
    52. 52)
      • 4. Saran, K.B., Sreelekha, G.: ‘Traffic video surveillance: vehicle detection and classification’, Int. Conf. on Control Communication and Computing, India, November 2015, pp. 516521.
    53. 53)
      • 98. Maddalena, L., Petrosino, A.: ‘A self organizing approach to background subtraction for visual surveillance applications’, IEEE Trans. Image Process., 2008, 17, (7), pp. 11681177.
    54. 54)
      • 93. Cordts, M., Omran, M., Ramos, S., et al: ‘The cityscapes dataset for semantic urban scene understanding’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016.
    55. 55)
      • 101. LeCun, Y., Bottou, L., Orr, G.B., et al: ‘Efficient backpropagation in neural networks’ (Tricks of the Trade, Springer, 1998), pp. 950.
    56. 56)
      • 73. Karpathy, A., Toderici, G., Shetty, S., et al: ‘Large-scale video classification with convolutional neural networks’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, USA, 2014, pp. 17251732.
    57. 57)
      • 54. Stauffer, C., Grimson, W.E.L.: ‘Adaptive background mixture models for real-time tracking’. Int. Conf. on Computer Vision and Pattern Recognition, Fort Collins, USA, 1999.
    58. 58)
      • 72. Girshick, R., Donahue, J., Darrell, T., et al: ‘Rich feature hierarchies for accurate object detection and semantic segmentation’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, USA, 2014, pp. 580587.
    59. 59)
      • 64. Wang, X., Zhang, J.: ‘A traffic incident detection method based on wavelet algorithm’. IEEE Workshop on Soft Computing in Industrial Applications, Espoo, Finland, 2005, pp. 166172.
    60. 60)
      • 102. Dong, Z., Wu, Y., Pei, M., et al: ‘Vehicle type classification using a semisupervised convolutional neural network’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 22472256.
    61. 61)
      • 29. Wang, Y.: ‘Joint random field model for all-weather moving vehicle detection’, IEEE Trans. Image Process., 2010, 19, (9), pp. 24912501.
    62. 62)
      • 47. Meyer, D., Denzler, J., Niemann, H.: ‘Model based extraction of articulated objects in image sequences for gait analysis’. Proc. IEEE Int. Conf. Image Processing, Santa Barbara, USA, 1998, pp. 7881.
    63. 63)
      • 96. Geiger, A., Lenz, P., Stiller, C., et al: ‘Vision meets robotics: the KITTI dataset’, Int. J. Robot. Res. (IJRR), 2013, 32, (11), pp. 12311237.
    64. 64)
      • 104. Zhang, F., Xu, X, Qiao, Y.: ‘Deep classification of vehicle makers and models: the effectiveness of Pre-training and data enhancement’. IEEE Int. Conf. on Robotics and Biomimetics (ROBIO), Zhuhai, China, 2015, pp. 231236.
    65. 65)
      • 109. Levin, A., Lischinski, D., Weiss, Y.: ‘A closed form solution to natural image matting’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, New York, USA, 2006, pp. 6168.
    66. 66)
      • 53. Benezeth, Y., Jodoin, P., Emile, B., et al: ‘Comparative study of background subtraction algorithms’, J. Electron. Imaging, Soc. Photo-Opt. Instrum. Eng., 2010, 19, (3), pp. 112.
    67. 67)
      • 82. Liou, C.Y., Huang, J.C., Yang, W.C.: ‘Modeling word perception using the Elman network’, Neurocomputing, 2008, 71, (1618), pp. 31503157.
    68. 68)
      • 25. http://www.dailymail.co.uk/news/article-2930654/Know-enemy-Incredibly-20-differentkindscameras-spying-motorists-spot-spotyou.html.
    69. 69)
      • 50. Chalidabhongse, T.H., Kim, K., Harwood, D.: ‘A perturbation method for evaluating background subtraction algorithms’. Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, Nice, France, 2003.
    70. 70)
      • 74. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’, 2014. Available at https://arxiv.org/abs/1409.1556.
    71. 71)
      • 105. Ambardekar, A., Nicolescu, M.: ‘Vehicle classification framework: a comparative study’, EURASIP J. Image Video Process., 2014, 29, pp. 113.
    72. 72)
      • 22. Cao, M., Vu, A., Barth, M.J.: ‘A novel omni-directional vision sensing technique for traffic surveillance’. IEEE Intelligent Transportation Systems Conf., Seattle, USA, 2007, pp. 678683.
    73. 73)
      • 8. https://i.ytimg.com/vi/xVwsr9p3irA/maxresdefault.jpg.
    74. 74)
      • 14. Deb, S.K., Nathr, R.K.: ‘Vehicle detection based on video for traffic surveillance on road’, Int. J. Comput. Sci. Emerg. Technol., 2012, 3, (4), pp. 121137.
    75. 75)
      • 43. Tan, T.N., Baker, K.D.: ‘Efficient image gradient based vehicle localization’, IEEE Trans. Image Process., 2000, 9, (11), pp. 13431356.
    76. 76)
      • 88. Suhao, L., Jinzhao, L., Guoquan, L., et al: ‘Vehicle type detection based on deep learning in traffic scene’, Procedia Comput. Sci., 2018, 131, pp. 564572.
    77. 77)
      • 36. Xua, H., Xia, X., Guo, L., et al: ‘A novel algorithm of moving cast shadow suppression’. Proc. of the 18 Int. Conf. on Signal Processing, Beijing, China, 2006, pp. 14.
    78. 78)
      • 41. Lee, J.W., Kim, M.S., Kweon, I.S.: ‘A Kalman filter based visual tracking algorithm for an object moving in 3-D’. Proc. Int. Conf. Intelligent Robots and Systems, Pittsburgh, USA, 1995, pp. 355358.
    79. 79)
      • 7. Lai, A.S.H., Yung, N.H.C.: ‘Vehicle-type identification through automated virtual loop assignment and block-based direction-biased motion estimation’, IEEE Trans. Intell. Transp. Syst., 2000, 1, (2), pp. 8697.
    80. 80)
      • 40. Kogut, G., Trivedi, M.: ‘A wide area tracking system for vision sensor networks’. The 9th World Congress Intelligent Transport Systems, Chicago, USA, 2002.
    81. 81)
      • 62. Cutler, R., Davis, L.: ‘Robust real-time periodic motion detection, analysis and applications’, IEEE Trans. Pattern Recognit. Mach. Intell., 2000, 13, pp. 129155.
    82. 82)
      • 68. Carrio, A., Sampedro, C., Rodrigues-Ramos, A., et al: ‘A review of deep learning methods and applications for unmanned aerial vehicles’, J. Sens., 2017, 1, pp. 113.
    83. 83)
      • 42. Costa, M.S., Shapiro, L.G.: ‘3-D object recognition and pose with relational indexing’, Comput. Vis. Image Underst., 2000, 79, (3), pp. 64407.
    84. 84)
      • 99. Lai, A.H.S., Fung, G.S.K., Yung, N.H.C.: ‘Vehicle type classification from visual-based dimension estimation’. Proc. of the IEEE Intelligent Transportation Systems Conf., Oakland, USA, 2001, pp. 201206.
    85. 85)
      • 56. Unzueta, L., Nieto, M., Cortes, A.: ‘Adaptive multi cue background subtraction for robust vehicle counting and classification’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (2), pp. 527540.
    86. 86)
      • 39. Lou, J., Tan, T., Hu, W., et al: ‘3-D model-based vehicle tracking’, IEEE Trans. Image Process., 2005, 14, (10), pp. 15611569.
    87. 87)
      • 60. Oliver, N.M., Rosario, B., Pentland, A.P.: ‘A Bayesian computer vision system for modelling human interactions’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (8), pp. 831843.
    88. 88)
      • 108. He, K., Sun, J., Tang, X.: ‘Single image haze removal using dark channel prior’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (12), pp. 23412353.
    89. 89)
      • 34. Liu, Z., Huang, K., Tan, T.: ‘Cast shadow removal in a hierarchical manner using MRF’, IEEE Trans. Circuits Syst. Video Technol., 2012, 22, (1), pp. 5666.
    90. 90)
      • 57. Yuan, W., Wang, J.: ‘Gaussian mixture model based on the number of moving vehicle detection algorithm’. Proc. of 2012 IEEE Int. Conf. on Intelligent Control Automatic Detection and High-End Equipment (ICADE), Beijing, China, 2012, pp. 9497.
    91. 91)
      • 3. Sun, Z., Bebis, G., Miller, R.: ‘On-road vehicle detection: a review’, IEEE Trans Pattern Anal. Mach. Intell., 2006, 28, (5), pp. 694711.
    92. 92)
      • 15. Michalopoulos, P.G.: ‘Vehicle detection video through image processing: the autoscope system’, IEEE Trans. Veh. Technol., 1991, 40, (1), pp. 2129.
    93. 93)
      • 28. Pang, C.C.C., Lam, W.W.L., Yung, N.H.C.: ‘A novel method for resolving vehicle occlusion in a monocular traffic-image sequence’, IEEE Trans. Intell. Transp. Syst., 2004, 5, (3), pp. 129141.
    94. 94)
      • 80. Freund, Y., Haussler, D.: ‘Unsupervised learning of distributions on binary vectors using two layers networksProc. 4th Int. Conf. on Neural Information Processing Systems (NIPS'91), Denver, USA, 1991, pp. 912919.
    95. 95)
      • 66. Fadlullah, Z.M., Tang, F., Mao, B., et al: ‘State-of-the-art deep learning: evolving machine intelligence toward tomorrow's intelligent network traffic control systems’, IEEE Commun. Surv. Tutor., 2017, 19, (4), pp. 24322455.
    96. 96)
      • 13. Baran, R., Rusc, T., Fornalski, P.: ‘A smart camera for the surveillance of vehicles in intelligent transportation systems’, Multimedia Tools Appl., 2016, 75, (17), pp. 1047110493.
    97. 97)
      • 103. Zhang, F.: ‘Car detection and vehicle type classification based on deep learning’ (Jiangsu University, China, 2016).
    98. 98)
      • 75. Szegedy, C., Liu, W., Jia, Y., et al: ‘Going deeper with convolutions’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Boston, USA, 2015, pp. 19.
    99. 99)
      • 89. Redmon, J., Divvala, S., Girshick, R., et al: ‘You only Look once: unified, real-time object detection’. IEEE Conf. on CVPR, Las Vegas, USA, 2016.
    100. 100)
      • 49. Shaikh, S.H., Saeed, K., Chaki, N.: ‘Moving object detection using background subtraction’ (Springer Briefs in Computer Science, New York, 2014).
    101. 101)
      • 83. Bourlard, H., Kamp, Y.: ‘Auto-association by multilayer perceptrons and singular value decomposition’, Biol. Cybern., 1988, 59, (4), pp. 291294.
    102. 102)
      • 26. Rai, M., Maity, T., Yadav, R.K.: ‘Thermal imaging system and its real-time applications: a survey’, J. Eng. Technol., 2017, 6, (2), pp. 290303.
    103. 103)
      • 86. Schmidhuber, J.: ‘Deep learning in neural networks: an overview’, Neural Netw., 2015, 61, pp. 85117.
    104. 104)
      • 79. LeCun, Y., Boser, B., Denker, J.S., et al: ‘Backpropagation applied to handwritten Zip code recognition’, Neural Comput., 1989, 1, (4), pp. 541551.
    105. 105)
      • 12. Rai, M., Yadav, R.K.: ‘A novel method for detection and extraction of human face for video surveillance applications’, Int. J. Signal Imaging Syst. Eng., 2016, 9, (3), pp. 165173.
    106. 106)
      • 44. Muller, K., Smolic, A., Drose, M., et al: ‘3-D construction of a dynamic environment with a fully calibrated background for traffic scenes’, IEEE Trans. Circuits Syst. Video Technol., 2005, 15, (4), pp. 538549.
    107. 107)
      • 46. Ghosh, N., Bhanu, B.: ‘Incremental vehicle 3-D modeling from video’. Proc. of the 18th Int. Conf. on Pattern Recognition, Hong Kong, China, 2006, pp. 272275.
    108. 108)
      • 37. Yoneyama, A., Yeh, C.H., Kuo, C.C.J.: ‘Moving cast shadow elimination for robust vehicle extraction based on 2D joint vehicle/shadow models’. Proc. IEEE Conf. Advanced Video and Signal Based Surveillance, Miami, USA, 2003, pp. 229236.
    109. 109)
      • 9. Kim, J.B., Kim, H.J.: ‘Efficient region-based motion segmentation for a video monitoring system’, Pattern Recognit. Lett., 2003, 24, (1–3), pp. 113128.
    110. 110)
      • 10. Wu, B.F., Juang, J.H.: ‘Adaptive vehicle detector approach for complex environments’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (2), pp. 817827.
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