Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

IEEE Transactions on Intelligent Transportation Systems

Volumn 16, Issue 2, 2015, Pages 557-580

  Tian, Bin ^a   Morris, Brendan Tran ^b   Tang, Ming ^a   Liu, Yuqiang ^a   Yao, Yanjie ^a   Gou, Chao ^a   Shen, Dayong ^c   Tang, Shaohu ^d  

^a INSTITUTE OF AUTOMATION   (China)

^b University of Nevada Las Vegas   (United States)

^c NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^d NORTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Behavior understanding; computer vision; networked surveillance system; traffic surveillance; vehicle detection; vehicle tracking

Indexed keywords

COMPLEX NETWORKS; COMPUTER VISION; HIERARCHICAL SYSTEMS; HIGHWAY TRAFFIC CONTROL; IMAGE PROCESSING; INTELLIGENT SYSTEMS; MONITORING; MOTOR TRANSPORTATION; SENSOR NODES; SURVEYS; TRACKING (POSITION); VEHICLES;

BEHAVIOR UNDERSTANDING; CLUTTERED BACKGROUNDS; INTELLIGENT TRANSPORTATION SYSTEMS; SURVEILLANCE SYSTEMS; TRAFFIC SURVEILLANCE; VEHICLE DETECTION; VIDEO BASED SURVEILLANCE; VIDEO SURVEILLANCE SYSTEMS;

SECURITY SYSTEMS;

EID: 84926504625     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2014.2340701     Document Type: Review

References (253)

1. N. Buch, S. A. Velastin, and J. Orwell, "A review of computer vision techniques for the analysis of urban traffic," IEEE Trans. Intell. Transp. Syst., vol. 12, no. 3, pp. 920-939, Sep. 2011.
2. X. Wang, "Intelligent multi-camera video surveillance: A review," Pattern Recognit. Lett., vol. 34, no. 1, pp. 3-19, Jan. 2013.
3. L.-W. Tsai, J.-W. Hsieh, and K.-C. Fan, "Vehicle detection using normalized color and edge map," IEEE Trans. Image Process., vol. 16, no. 3, pp. 850-864, Mar. 2007.
4. R. Cucchiara, M. Piccardi, and P. Mello, "Image analysis and rule-based reasoning for a traffic monitoring system," IEEE Trans. Intell. Transp. Syst., vol. 1, no. 2, pp. 119-130, Jun. 2000.
5. S. Gupte, O. Masoud, R. F. K. Martin, and N. P. Papanikolopoulos, "Detection and classification of vehicles," IEEE Trans. Intell. Transp. Syst., vol. 3, no. 1, pp. 37-47, Mar. 2002.
6. A. Ottlik and H.-H. Nagel, "Initialization of model-based vehicle tracking in video sequences of inner-city intersections," Int. J. Comput. Vis., vol. 80, no. 2, pp. 211-225, Nov. 2008.
7. X. Ma and W. E. L. Grimson, "Edge-based rich representation for vehicle classification," in Proc. IEEE Int. Conf. Comput. Vis., 2005, vol. 2, pp. 1185-1192.
8. N. Buch, J. Orwell, and S. Velastin, "3D extended Histogram of Oriented Gradients (3DHOG) for classification of road users in urban scenes," in Proc. Brit. Mach. Vis. Conf., London, U.K., 2009, pp. 1-11.
9. R. S. Feris et al., "Large-scale vehicle detection, indexing, and search in urban surveillance videos," IEEE Trans. Multimedia, vol. 14, no. 1, pp. 28-42, Feb. 2012.
10. G. Marola, "Using symmetry for detecting and locating objects in a picture," Comput. Vis. Graph. Image Process., vol. 46, no. 2, pp. 179-195, May 1989.
11. A. Kuehnle, "Symmetry-based recognition of vehicle rears," Pattern Recognit. Lett., vol. 12, no. 4, pp. 249-258, Apr. 1991.
12. W. Von Seelen, C. Curio, J. Gayko, U. Handmann, and T. Kalinke, "Scene analysis and organization of behavior in driver assistance systems,"in Proc. Int. Conf. Image Process., 2000, vol. 3, pp. 524-527.
13. D. Guo, T. Fraichard, M. Xie, and C. Laugier, "Color modeling by spherical influence field in sensing driving environment," in Proc. IEEE Intell. Veh. Symp., 2000, pp. 249-254.
14. N. Matthews, P. An, D. Charnley, and C. Harris, "Vehicle detection and recognition in greyscale imagery," Control Eng. Pract., vol. 4, no. 4, pp. 473-479, Apr. 1996.
15. C. Goerick, D. Noll, and M. Werner, "Artificial neural networks in realtime car detection and tracking applications," Pattern Recognit. Lett., vol. 17, no. 4, pp. 335-343, Apr. 1996.
16. N. Srinivasa, "Vision-based vehicle detection and tracking method for forward collision warning in automobiles," in Proc. IEEE Intell. Veh. Symp., 2002, vol. 2, pp. 626-631.
17. M. Bertozzi, A. Broggi, and S. Castelluccio, "A real-time oriented system for vehicle detection," J. Syst. Archit., vol. 43, no. 1-5, pp. 317-325, Mar. 1997.
18. T. Kalinke, C. Tzomakas, and W. V. Seelen, "A texture-based object detection and an adaptive model-based classification," in Proc. IEEE Int. Conf. Intell. Veh., 1998, pp. 143-148.
19. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural features for image classification," IEEE Trans. Syst., Man, Cybern., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
20. S. Agarwal, A. Awan, and D. Roth, "Learning to detect objects in images via a sparse, part-based representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1475-1490, Nov. 2004.
21. D. G. Lowe, "Object recognition from local scale-invariant features," in Proc. IEEE Int. Conf. Comput. Vis., 1999, vol. 2, pp. 1150-1157.
22. Z. Kim and J. Malik, "Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking," in Proc. IEEE Int. Conf. Comput. Vis., 2003, pp. 524-531.
23. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2005, vol. 1, pp. 886-893.
24. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2001, vol. 1, pp. 511-518.
25. P. Viola and M. J. Jones, "Robust real-time face detection," Int. J. Comput. Vis., vol. 57, no. 2, pp. 137-154, May 2004.
26. C. Szegedy, A. Toshev, and D. Erhan, "Deep neural networks for object detection," in Proc. Adv. Neural Inf. Process. Syst., 2013, pp. 2553-2561.
27. R. Socher, B. Huval, B. P. Bath, C. D. Manning, and A. Y. Ng, "Convolutional-recursive deep learning for 3D object classification," in Proc. Neural Inf. Process. Syst., 2012, pp. 665-673.
28. Z. Chen, T. Ellis, and S. A. Velastin, "Vehicle detection, tracking and classification in urban traffic," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2012, pp. 951-956.
29. Y. Freund, "Boosting a weak learning algorithm by majority," Inf. Comput., vol. 121, no. 2, pp. 256-285, Sep. 1995.
30. J. Winn and J. Shotton, "The layout consistent random field for recognizing and segmenting partially occluded objects," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2006, vol. 1, pp. 37-44.
31. D. Hoiem, C. Rother, and J. Winn, "3D layoutcrf for multi-view object class recognition and segmentation," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2007, pp. 1-8.
32. S. Kamijo, Y. Matsushita, K. Ikeuchi, and M. Sakauchi, "Traffic monitoring and accident detection at intersections," IEEE Trans. Intell. Transp. Syst., vol. 1, no. 2, pp. 108-118, Jun. 2000.
33. C.-C. R. Wang and J. J. J. Lien, "Automatic vehicle detection using local features: A statistical approach," IEEE Trans. Intell. Transp. Syst., vol. 9, no. 1, pp. 83-96, Mar. 2008.
34. Y. N. Wu, Z. Si, H. Gong, and S.-C. Zhu, "Learning active basis model for object detection and recognition," Int. J. Comput. Vis., vol. 90, no. 2, pp. 198-235, Nov. 2010.
35. S. Sivaraman and M. M. Trivedi, "Vehicle detection by independent parts for urban driver assistance," IEEE Trans. Intell. Transp. Syst., vol. 14, no. 4, pp. 1597-1608, Dec. 2013.
36. L. Lin, T. Wu, J. Porway, and Z. Xu, "A stochastic graph grammar for compositional object representation and recognition," Pattern Recognit., vol. 42, no. 7, pp. 1297-1307, Jul. 2009.
37. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models,"IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627-1645, Sep. 2010.
38. P. F. Felzenszwalb, R. B. Girshick, and D. McAllester, "Cascade object detection with deformable part models," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 2241-2248.
39. H. T. Niknejad, T. Kawano, Y. Oishi, and S. Mita, "Occlusion handling using discriminative model of trained part templates and conditional random field," in Proc. IEEE Intell. Veh. Symp., 2013, pp. 750-755.
40. L. Yang, B. Yao, W. Yongtian, and S.-C. Zhu, "Reconfigurable templates for robust vehicle detection and classification," in Proc. IEEE Workshop Appl. Comput. Vis., 2012, pp. 321-328.
41. G. D. Sullivan, K. D. Baker, A. D. Worrall, C. Attwood, and P. Remagnino, "Model-based vehicle detection and classification using orthographic approximations," Image Vis. Comput., vol. 15, no. 8, pp. 649-654, Aug. 1997.
42. S. Messelodi, C. M. Modena, and M. Zanin, "A computer vision system for the detection and classification of vehicles at urban road intersections,"Pattern Anal. Appl., vol. 8, no. 1/2, pp. 17-31, Sep. 2005.
43. X. Song and R. Nevatia, "Detection and tracking of moving vehicles in crowded scenes," in Proc. IEEE Workshop Motion Video Comput., 2007, pp. 1-4.
44. B. Johansson, J. Wiklund, P.-E. Forssén, and G. Granlund, "Combining shadow detection and simulation for estimation of vehicle size and position,"Pattern Recognit. Lett., vol. 30, no. 8, pp. 751-759, Jun. 2009.
45. M. J. Leotta and J. L. Mundy, "Predicting high resolution image edges with a generic, adaptive, 3-D vehicle model," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2009, pp. 1311-1318.
46. J. Liebelt, C. Schmid, and K. Schertler, "Viewpoint-independent object class detection using 3D feature maps," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2008, pp. 1-8.
47. A. Lai and N. H. C. Yung, "A fast and accurate scoreboard algorithm for estimating stationary backgrounds in an image sequence," in Proc. IEEE Int. Symp. Circuits Syst., May 1998, vol. 4, pp. 241-244.
48. P. Kumar, S. Ranganath, and H. Weimin, "Bayesian network based computer vision algorithm for traffic monitoring using video," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2003, vol. 1, pp. 897-902.
49. B. T. Morris and M. M. Trivedi, "Learning, modeling, and classification of vehicle track patterns from live video," IEEE Trans. Intell. Transp. Syst., vol. 9, no. 3, pp. 425-437, Sep. 2008.
50. C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time tracking," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 1999, vol. 2, p. 252.
51. J. Zheng, Y. Wang, N. L. Nihan, and M. E. Hallenbeck, "Extracting roadway background image: Mode-based approach," J. Transp. Res. Board, vol. 1944, no. 1, pp. 82-88, 2006.
52. T. Gao, Z.-G. Liu, W.-C. Gao, and J. Zhang, "A robust technique for background subtraction in traffic video," in Advances in Neuro-Information Processing. Berlin, Germany: Springer-Verlag, 2009, pp. 736-744.
53. C.-L. Huang and W.-C. Liao, "A vision-based vehicle identification system,"in Proc. Int. Conf. Pattern Recog., 2004, vol. 4, pp. 364-367.
54. A. Yoneyama, C.-H. Yeh, and C.-C. J. Kuo, "Robust vehicle and traffic information extraction for highway surveillance," EURASIP J. Appl. Signal Process., vol. 14, no. 1, pp. 2305-2321, Jan. 2005.
55. R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, "Detecting moving objects, ghosts, and shadows in video streams," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 10, pp. 1337-1342, Oct. 2003.
56. X. Ji, Z. Wei, and Y. Feng, "Effective vehicle detection technique for traffic surveillance systems," J. Vis. Commun. Image Represent., vol. 17, no. 3, pp. 647-658, Jun. 2006.
57. A. Prati, I. Mikic, M. M. Trivedi, and R. Cucchiara, "Detecting moving shadows: Algorithms and evaluation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 7, pp. 918-923, Jul. 2003.
58. W. Zhang, Q. J. Wu, X. Yang, and X. Fang, "Multilevel framework to detect and handle vehicle occlusion," IEEE Trans. Intell. Transp. Syst., vol. 9, no. 1, pp. 161-174, Mar. 2008.
59. N. K. Kanhere and S. T. Birchfield, "Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features,"IEEE Trans. Intell. Transp. Syst., vol. 9, no. 1, pp. 148-160, Mar. 2008.
60. C. C. C. Pang, W. W. L. Lam, and N. H. C. Yung, "A method for vehicle count in the presence of multiple-vehicle occlusions in traffic images,"IEEE Trans. Intell. Transp. Syst., vol. 8, no. 3, pp. 441-459, Sep. 2007.
61. G. Gritsch, N. Donath, B. Kohn, and M. Litzenberger, "Night-time vehicle classification with an embedded, vision system," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2009, pp. 1-6.
62. K. Robert, "Video-based traffic monitoring at day and night vehicle features detection tracking," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2009, pp. 1-6.
63. K. Robert, "Night-time traffic surveillance: A robust framework for multi-vehicle detection, classification and tracking," in Proc. IEEE Int. Conf. Adv. Video Signal Based Surveillance, 2009, pp. 1-6.
64. W. Wang, C. Shen, J. Zhang, and S. Paisitkriangkrai, "A two-layer nighttime vehicle detector," in Proc. Digit. Image Comput., Tech. Appl., 2009, pp. 162-167.
65. Y.-L. Chen, B.-F. Wu, and C.-J. Fan, "Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance," in Proc. IEEE Int. Conf. Syst., Man Cybern., 2009, pp. 3352-3358.
66. B.-F. Wu, Y.-L. Chen, and C.-C. Chiu, "A discriminant analysis based recursive automatic thresholding approach for image segmentation," IEICE Trans. Inf. Syst., vol. E88-D, no. 7, pp. 1716-1723, Jul. 2005.
67. W. Zhang, Q. M. J. Wu, G. Wang, and X. You, "Tracking and pairing vehicle headlight in night scenes," IEEE Trans. Intell. Transp. Syst., vol. 13, no. 1, pp. 140-153, Mar. 2012.
68. L. Unzueta et al., "Adaptive multicue background subtraction for robust vehicle counting and classification," IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 527-540, Jun. 2012.
69. F. Bardet and T. Chateau, "MCMC particle filter for real-time visual tracking of vehicles," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2008, pp. 539-544.
70. E. Maggio and A. Cavallaro, "Hybrid particle filter and mean shift tracker with adaptive transition model," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2005, pp. 19-23.
71. N. Wang and D.-Y. Yeung, "Learning a deep compact image representation for visual tracking," in Proc. Adv. Neural Inf. Process. Syst., 2013, pp. 809-817.
72. D. Roller, K. Daniilidis, and H.-H. Nagel, "Model-based object tracking in monocular image sequences of road traffic scenes," Int. J. Comput. Vis., vol. 10, no. 3, pp. 257-281, Jun. 1993.
73. R. E. Kalman, "A new approach to linear filtering and prediction problems," Trans. ASME, J. Basic Eng., vol. 82, no. 1, pp. 35-45, Mar. 1960.
74. R. Rad and M. Jamzad, "Real time classification and tracking of multiple vehicles in highways," Pattern Recognit. Lett., vol. 26, no. 10, pp. 1597-1607, Jul. 2005.
75. J. Lou, H. Yang, W. M. Hu, and T. Tan, "Visual vehicle tracking using an improved EKF," in Proc. Asian Conf. Comput. Vis., 2002, pp. 296-301.
76. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian bayesian tracking,"IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174-188, Feb. 2002.
77. K. Nummiaro, E. Koller-Meier, and L. Van Gool, "An adaptive color-based particle filter," Image Vis. Comput., vol. 21, no. 1, pp. 99-110, Jan. 2003.
78. T. Mauthner, M. Donoser, and H. Bischof, "Robust tracking of spatial related components," in Proc. Int. Conf. Pattern Recog., 2008, pp. 1-4.
79. S. Khatoonabadi and I. Bajic, "Video object tracking in the compressed domain using spatio-temporal Markov random fields," IEEE Trans. Image Process., vol. 22, no. 1, pp. 300-313, Jan. 2013.
80. D. Zheng, Y. Zhao, and J. Wang, "An efficient method of license plate location," Pattern Recognit. Lett., vol. 26, no. 15, pp. 2431-2438, Nov. 2005.
81. S.-Z. Wang and H.-J. Lee, "Detection and recognition of license plate characters with different appearances," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2003, vol. 2, pp. 979-984.
82. F. Faradji, A. H. Rezaie, and M. Ziaratban, "A morphological-based license plate location," in Proc. IEEE Int. Conf. Image Process., 2007, vol. 1, pp. 57-60.
83. C. N. K. Babu and K. Nallaperumal, "An efficient geometric feature based license plate localization and recognition," Int. J. Imag. Sci. Eng., vol. 2, no. 2, pp. 189-194, Apr. 2008.
84. V. Kamat and S. Ganesan, "An efficient implementation of the hough transform for detecting vehicle license plates using DSP's," in Proc. Real-Time Technol. Appl. Symp., 1995, pp. 58-59.
85. A. M. Al-Ghaili, S. Mashohor, A. Ismail, and A. R. Ramli, "A new vertical edge detection algorithm and its application," in Proc. Int. Conf. Comput. Eng. Syst., 2008, pp. 204-209.
86. H. Zhang, W. Jia, X. He, and Q. Wu, "Learning-based license plate detection using global and local features," in Proc. Int. Conf. Pattern Recog., 2006, vol. 2, pp. 1102-1105.
87. W. Le and S. Li, "A hybrid license plate extraction method for complex scenes," in Proc. Int. Conf. Pattern Recog., 2006, vol. 2, pp. 324-327.
88. L. Dlagnekov, "License plate detection using adaboost," Dept. Comput. Sci. Eng., Univ. California San Diego, La Jolla, CA, USA, 2004.
89. J. Jiao, Q. Ye, and Q. Huang, "A configurable method for multi-style license plate recognition," Pattern Recognit., vol. 42, no. 3, pp. 358-369, Mar. 2009.
90. X. Shi, W. Zhao, and Y. Shen, "Automatic license plate recognition system based on color image processing," in Computational Science and Its Applications. Berlin, Germany: Springer-Verlag, 2005, pp. 1159-1168.
91. G. Cao, J. Chen, and J. Jiang, "An adaptive approach to vehicle license plate localization," in Proc. IEEE Annu. Conf. Ind. Electron. Soc., 2003, vol. 2, pp. 1786-1791.
92. X. Wan, J. Liu, and J. Liu, "A vehicle license plate localization method using color barycenters hexagon model," in Proc. SPIE Int. Soc. Opt. Photon., 2011, pp. 80092O-1-80092O-5.
93. K. Deb and K.-H. Jo, "A vehicle license plate detection method for intelligent transportation system applications," Cybern. Syst., vol. 40, no. 8, pp. 689-705, Nov. 2009.
94. D. Llorens, A. Marzal, V. Palazon, and J. M. Vilar, "Car license plates extraction and recognition based on connected components analysis and hmm decoding," in Pattern Recognition and Image Analysis. Berlin, Germany: Springer-Verlag, 2005, pp. 571-578.
95. K. K. Kim, K. Kim, J. Kim, and H. Kim, "Learning-based approach for license plate recognition," in Proc. IEEE Signal Process. Soc. Workshop Neur. Netw. Signal Process., 2000, vol. 2, pp. 614-623.
96. M.-P. D. Jolly, S. Lakshmanan, and A. K. Jain, "Vehicle segmentation and classification using deformable templates," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 3, pp. 293-308, Mar. 1996.
97. D. T. Munroe and M. G. Madden, "Multi-class and single-class classification approaches to vehicle model recognition from images,"in Proc. Irish Conf. Artif. Intell. Cogn. Sci., Londonderry, U.K., 2005.
98. I. Zafar, E. A. Edirisinghe, S. Acar, and H. E. Bez, "Two-dimensional statistical linear discriminant analysis for real-time robust vehicletype recognition," in Proc. SPIE Real-Time Image Process., 2007, pp. 649 602-1-649 602-8.
99. H. Huang, Q. Zhao, Y. Jia, and S. Tang, "A 2dlda based algorithm for real time vehicle type recognition," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2008, pp. 298-303.
100. 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 Recog., 2006, vol. 1, pp. 574-577.
101. R. Feris, J. Petterson, B. Siddiquie, L. Brown, and S. Pankanti, "Large-scale vehicle detection in challenging urban surveillance environments,"in Proc. IEEE Workshop Appl. Comput. Vis., 2011, pp. 527-533.
102. B. Zhang and C. Zhao, "Classification of vehicle make by combined features and random subspace ensemble," in Proc. Int. Conf. Image Graph., 2011, pp. 920-925.
103. O. Hasegawa and T. Kanade, "Type classification, color estimation, and specific target detection of moving targets on public streets," Mach. Vis. Appl., vol. 16, no. 2, pp. 116-121, Feb. 2005.
104. N. Baek, S.-M. Park, K.-J. Kim, and S.-B. Park, "Vehicle color classification based on the support vector machine method," in Communications in Computer and Information Science, vol. 2. Berlin, Germany: Springer-Verlag, 2007, pp. 1133-1139.
105. E. Dule, M. Gokmen, and M. S. Beratoglu, "A convenient feature vector construction for vehicle color recognition," in Proc. Int. Conf. Neural Netw. WSEAS, 2010, pp. 250-255.
106. H. J. Lee, "Neural network approach to identify model of vehicles,"in Advances in Neural Networks. Berlin, Germany: Springer-Verlag, 2006, pp. 66-72.
107. A. Psyllos, C. Anagnostopoulos, E. Kayafas, and V. Loumos, "Image processing & artificial neural networks for vehicle make and model recognition," in Proc. Conf. Appl. Adv. Tech. Transp., 2008, vol. 5, pp. 4229-4243.
108. th IMEKO TC4 Symp., 2008, pp. 1-6.
109. Y. Wang, N. Li, and Y. Wu, "Application of edge testing operator in vehicle logo recognition," in Proc. Int. Workshop Intell. Syst. Appl., 2010, pp. 1-3.
110. Y. Wang, Z. Liu, and F. Xiao, "A fast coarse-to-fine vehicle logo detection and recognition method," in Proc. IEEE Int. Conf. Robot. Biomimetics, 2007, pp. 691-696.
111. H.-K. Xu, F.-H. Yu, J.-H. Jiao, and H.-S. Song, "A new approach of the vehicle license plate location," in Proc. Int. Conf. Parallel Distrib. Comput. Appl. Technol., 2005, pp. 1055-1057.
112. C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos, and E. Kayafas, "A license plate-recognition algorithm for intelligent transportation system applications," IEEE Trans. Intell. Transp. Syst., vol. 7, no. 3, pp. 377-392, Sep. 2006.
113. S. Yohimori, Y. Mitsukura, M. Fukumi, N. Akamatsu, and N. Pedrycz, "License plate detection system by using threshold function and improved template matching method," in Proc. IEEE Annu. Meet. Fuzzy Inf., 2004, vol. 1, pp. 357-362.
114. W. Jia, H. Zhang, X. He, and Q. Wu, "Gaussian weighted histogram intersection for license plate classification," in Proc. Int. Conf. Pattern Recog., 2006, vol. 3, pp. 574-577.
115. J.-F. Xu, S.-F. Li, and M.-S. Yu, "Car license plate extraction using color and edge information," in Proc. IEEE Int. Symp. Ind. Electron., 2004, vol. 6, pp. 3904-3907.
116. Z.-X. Chen, C.-Y. Liu, F.-L. Chang, and G.-Y. Wang, "Automatic license-plate location and recognition based on feature salience," IEEE Trans. Veh. Technol., vol. 58, no. 7, pp. 3781-3785, Sep. 2009.
117. Y. Lee et al., "License plate detection using local structure patterns,"in Proc. IEEE Int. Conf. Adv. Video Signal Based Surveillance, 2010, pp. 574-579.
118. Q. Gao, X. Wang, and G. Xie, "License plate recognition based on prior knowledge," in Proc. IEEE Int. Conf. Autom. Logist., 2007, pp. 2964-2968.
119. J.-M. Guo and Y.-F. Liu, "License plate localization and character segmentation with feedback self-learning and hybrid binarization techniques,"IEEE Trans. Veh. Technol., vol. 57, no. 3, pp. 1417-1424, May 2008.
120. T. Nukano, M. Fukumi, and M. Khalid, "Vehicle license plate character recognition by neural networks," in Proc. Int. Symp. Intell. Signal Process. Commun. Syst., 2004, pp. 771-775.
121. V. Shapiro and G. Gluhchev, "Multinational license plate recognition system: Segmentation and classification," in Proc. Int. Conf. Pattern Recog., 2004, vol. 4, pp. 352-355.
122. B.-F. Wu, S.-P. Lin, and C.-C. Chiu, "Extracting characters from real vehicle licence plates out-of-doors," IET Comput. Vis., vol. 1, no. 1, pp. 2-10, Mar. 2007.
123. P. Soille, Morphological Image Analysis: Principles and Applications. New York, NY, USA: Springer-Verlag, 2003.
124. S. Nomura, K. Yamanaka, O. Katai, H. Kawakami, and T. Shiose, "A novel adaptive morphological approach for degraded character image segmentation," Pattern Recognit., vol. 38, no. 11, pp. 1961-1975, Nov. 2005.
125. M.-S. Pan, J.-B. Yan, and Z.-H. Xiao, "Vehicle license plate character segmentation," Int. J. Autom. Comput., vol. 5, no. 4, pp. 425-432, Oct. 2008.
126. Z. Ping and C. Lihui, "A novel feature extraction method and hybrid tree classification for handwritten numeral recognition," Pattern Recognit. Lett., vol. 23, no. 1-3, pp. 45-56, Jan. 2002.
127. H. Erdinc Kocer and K. Kursat Cevik, "Artificial neural networks based vehicle license plate recognition," Proc. Comput. Sci., vol. 3, pp. 1033-1037, 2011.
128. J. Cao, M. Ahmadi, and M. Shridhar, "Recognition of handwritten numerals with multiple feature and multistage classifier," Pattern Recognit., vol. 28, no. 2, pp. 153-160, Feb. 1995.
129. Y. S. Huang and C. Y. Suen, "A method of combining multiple experts for the recognition of unconstrained handwritten numerals,"IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 1, pp. 90-94, Jan. 1995.
130. H.-J. Kang, K. Kim, and J. H. Kim, "A framework for probabilistic combination of multiple classifiers at an abstract level," Eng. Appl. Artif. Intell., vol. 10, no. 4, pp. 379-385, Aug. 1997.
131. C.-N. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psoroulas, V. Loumos, and E. Kayafas, "License plate recognition from still images and video sequences: A survey," IEEE Trans. Intell. Transp. Syst., vol. 9, no. 3, pp. 377-391, Sep. 2008.
132. S. Sivaraman and M. M. Trivedi, "A general active-learning framework for on-road vehicle recognition and tracking," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 2, pp. 267-276, Jun. 2010.
133. B. Morris, C. Tran, G. Scora, M. Trivedi, and M. Barth, "Realtime video-based traffic measurement and visualization system for energy/emissions," IEEE Trans. Intell. Transp. Syst., vol. 13, no. 4, pp. 1667-1678, Dec. 2012.
134. R. P. Avery, Y. Wang, and G. S. Rutherford, "Length-based vehicle classification using images from uncalibrated video cameras," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2004, pp. 737-742.
135. D. Han, M. J. Leotta, D. B. Cooper, and J. L. Mundy, "Vehicle class recognition from video-based on 3d curve probes," in Proc. IEEE Int. Workshop Vis. Surveillance Perform. Eval. Tracking Surveillance, 2005, pp. 285-292.
136. V. S. Petrovic and T. F. Cootes, "Analysis of features for rigid structure vehicle type recognition," in Proc. Brit. Mach. Vis. Conf., 2004, pp. 1-10.
137. 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.
138. H. Yang et al., "An efficient method for vehicle model identification via logo recognition," in Proc. Int. Conf. Comput. Inf. Sci., 2013, pp. 1080-1083.
139. A. P. Psyllos, C.-N. 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.
140. A. Minich and C. Li, "Vehicle logo recognition and classification: Feature descriptors vs. shape descriptors," Stanford Univ., Stanford, CA, USA, EE368 Final Proj., 2011.
141. R. Eshel and Y. Moses, "Homography based multiple camera detection and tracking of people in a dense crowd," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2008, pp. 1-8.
142. A. D. Straw, K. Branson, T. R. Neumann, and M. H. Dickinson, "Multi-camera real-time three-dimensional tracking of multiple flying animals," J. R. Soc. Interface, vol. 8, no. 56, pp. 395-409, Mar. 2011.
143. B. C. Matei, H. S. Sawhney, and S. Samarasekera, "Vehicle tracking across nonoverlapping cameras using joint kinematic and appearance features," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 3465-3472.
144. R. Hamid et al., "Player localization using multiple static cameras for sports visualization," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 731-738.
145. B. Prosser, W.-S. Zheng, S. Gong, T. Xiang, and Q. Mary, "Person reidentification by support vector ranking," in Proc. Brit. Mach. Vis. Conf., 2010, vol. 2, p. 6.
146. A. Civilis, C. S. Jensen, J. Nenortaite, and S. Pakalnis, "Efficient tracking of moving objects with precision guarantees," in Proc. Int. Conf. Mobile Ubiquitous Syst., 2004, pp. 164-173.
147. A. Civilis, C. S. Jensen, and S. Pakalnis, "Techniques for efficient road-network-based tracking of moving objects," IEEE Trans. Knowl. Data Eng., vol. 17, no. 5, pp. 698-712, May 2005.
148. R. Lange, F. Dü, and K. Rothermel, "Efficient real-time trajectory tracking,"VLDB J., vol. 20, no. 5, pp. 671-694, Oct. 2011.
149. A. Bobick and J. Davis, "The recognition of human movement using temporal templates," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 3, pp. 257-267, Mar. 2001.
150. P. Borges, N. Conci, and A. Cavallaro, "Video-based human behavior understanding: A survey," IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 11, pp. 1993-2008, Nov. 2013.
151. S. Vishwakarma and A. Agrawal, "A survey on activity recognition and behavior understanding in video surveillance," Vis. Comput., vol. 29, no. 10, pp. 983-1009, Oct. 2013.
152. T. Xiang and S. Gong, "Video behavior profiling for anomaly detection,"IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 5, pp. 893-908, May 2008.
153. V. S. P. Turaga, R. Chellappa, and O. Udrea, "Machine recognition of human activities: A survey," IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 11, pp. 1473-1488, Nov. 2008.
154. W. Hu, T. Tan, L. Wang, and S. Maybank, "A survey on visual surveillance of object motion and behaviors," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 34, no. 3, pp. 334-352, Aug. 2004.
155. H. Veeraraghavan and N. P. Papanikolopoulos, "Learning to recognize video-based spatiotemporal events," IEEE Trans. Intell. Transp. Syst., vol. 10, no. 4, pp. 628-638, Dec. 2009.
156. W. Hu, X. Li, G. Tian, S. J. Maybank, and Z. Zhang, "An incremental dpmm-based method for trajectory clustering, modeling, and retrieval,"IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 5, pp. 1051-1065, May 2013.
157. M. M. Trivedi, T. L. Gandhi, and K. S. Huang, "Distributed interactive video arrays for event capture and enhanced situational awareness,"IEEE Intell. Syst., vol. 20, no. 5, pp. 58-66, Sep. 2005.
158. P. Remagnino, S. A. Velastin, G. L. Foresti, and M. Trivedi, "Novel concepts and challenges for the next generation of video surveillance systems," Mach. Vis. Appl., vol. 18, no. 3, pp. 135-137, Aug. 2007.
159. K. A. Eccles, "Automated enforcement for speeding and red light running,"J. Transp. Res. Board, vol. 729, 2012.
160. B. E. Porter and K. J. England, "Predicting red-light running behavior: A traffic safety study in three urban settings," J. Safety Res., vol. 31, no. 1, pp. 1-8, 2000.
161. D. Kasper et al., "Object-oriented Bayesian networks for detection of lane change maneuvers," in Proc. IEEE Intell. Veh. Symp., 2011, pp. 673-678.
162. S. Kamijo, H. Koo, X. Liu, K. Fujihira, and M. Sakauchi, "Development and evaluation of real-time video surveillance system on highway based on semantic hierarchy and decision surface," in Proc. IEEE Int. Conf. Syst., Man Cybern., 2005, vol. 1, pp. 840-846.
163. X. Wang, K. Tieu, and E. Grimson, Learning Semantic Scene Models by Trajectory Analysis. Berlin, Germany: Springer-Verlag, 2006, pp. 110-123.
164. F. Jiang, Y. Wu, and A. K. Katsaggelos, "Abnormal event detection from surveillance video by dynamic hierarchical clustering," in Proc. IEEE Int. Conf. Image Process., 2007, vol. 5, pp. V-145-V-148.
165. C. Piciarelli, C. Micheloni, and G. L. Foresti, "Trajectory-based anomalous event detection," IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 11, pp. 1544-1554, Nov. 2008.
166. H. Huang, Z. Cai, S. Shi, X. Ma, and Y. Zhu, "Automatic detection of vehicle activities based on particle filter tracking," in Proc. ISCSCT, 2009, vol. 2, no. 2, p. 2.
167. M. Pucher et al., "Multimodal highway monitoring for robust incident detection," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2010, pp. 837-842.
168. C.-T. Hsieh, S.-B. Hsu, C.-C. Han, and K.-C. Fan, "Abnormal event detection using trajectory features," J. Inf. Technol. Appl., vol. 5, no. 1, pp. 22-27, 2011.
169. B. T. Morris and M. M. Trivedi, "A survey of vision-based trajectory learning and analysis for surveillance," IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 8, pp. 1114-1127, Aug. 2008.
170. T. Ko, "A survey on behavior analysis in video surveillance for home-land security applications," in Proc. IEEE Appl. Imagery Pattern Recog. Workshop, 2008, pp. 1-8.
171. C. Stauffer and W. E. L. Grimson, "Learning patterns of activity using real-time tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 747-757, Aug. 2000.
172. W. Hu, D. Xie, T. Tan, and S. Maybank, "Learning activity patterns using fuzzy self-organizing neural network," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 3, pp. 1618-1626, Jun. 2004.
173. D. Vasquez and T. Fraichard, "Motion prediction for moving objects: A statistical approach," in Proc. IEEE Int. Conf. Robot. Autom., 2004, vol. 4, pp. 3931-3936.
174. S. Atev, G. Miller, and N. P. Papanikolopoulos, "Clustering of vehicle trajectories," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 647-657, Sep. 2010.
175. S. Gong and T. Xiang, "Recognition of group activities using dynamic probabilistic networks," in Proc. IEEE Int. Conf. Comput. Vis., 2003, pp. 742-749.
176. Y. Xiong and D.-Y. Yeung, "Mixtures of ARMA models for model-based time series clustering," in Proc. IEEE Int. Conf. Data Mining, 2002, pp. 717-720.
177. M. Vlachos, G. Kollios, and D. Gunopulos, "Discovering similar multidimensional trajectories," in Proc. Int. Conf. Data Eng., 2002, pp. 673-684.
178. F. Bashir, A. Khokhar, and D. Schonfeld, "Real-time motion trajectory-based indexing and retrieval of video sequences," IEEE Trans. Multimedia, vol. 9, no. 1, pp. 58-65, Jan. 2007.
179. Y. Cao, C. Wang, L. Zhang, and L. Zhang, "Edgel index for large-scale sketch-based image search," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 761-768.
180. L. Olsen, F. F. Samavati, M. C. Sousa, and J. A. Jorge, "Sketch-based modeling: A survey," Comput. Graph., vol. 33, no. 1, pp. 85-103, Feb. 2009.
181. V. Saligrama, J. Konrad, and P.-M. Jodoin, "Video anomaly identification,"IEEE Signal Process. Mag., vol. 27, no. 5, pp. 18-33, Sep. 2010.
182. Y.-K. Jung and Y.-S. Ho, "Traffic parameter extraction using video-based vehicle tracking," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 1999, pp. 764-769.
183. R. Collins, A. Lipton, H. Fujiyoshi, and T. Kanade, "Algorithms for cooperative multisensor surveillance," Proc. IEEE, vol. 89, no. 10, pp. 1456-1477, Oct. 2001.
184. H. Aghajan and A. Cavallaro, Multi-Camera Networks: Concepts and Applications. Amsterdam, The Netherlands: Elsevier, 2009.
185. M. Valera and S. Velastin, "Intelligent distributed surveillance systems: A review," Proc. Inst. Elect. Eng.-Vis. Image Signal Process., vol. 152, no. 2, pp. 192-204, Apr. 2005.
186. X. Wang, K. Tieu, and E. Grimson, "Correspondence-free activity analysis and scene modeling in multiple camera views," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 56-71, Jan. 2010.
187. M. Vazirgiannis and O. Wolfson, "A spatiotemporal model and language for moving objects on road networks," in Advances in Spatial and Temporal Databases. Berlin, Germany: Springer-Verlag, 2001, pp. 20-35.
188. L. Speicvcys, C. S. Jensen, and A. Kligys, "Computational data modeling for network-constrained moving objects," in Proc. Int. Symp. Adv. Geograph. Inf. Syst., 2003, pp. 118-125.
189. J. Gudmundsson, M. van Kreveld, and B. Speckmann, "Efficient detection of motion patterns in spatio-temporal data sets," in Proc. Annu. ACM Int. Workshop Geograph. Inf. Syst., 2004, pp. 250-257.
190. P. Kalnis, N. Mamoulis, and S. Bakiras, "On discovering moving clusters in spatio-temporal data," in Advances in Spatial and Temporal Databases. Berlin, Germany: Springer-Verlag, 2005, pp. 364-381.
191. J. Gudmundsson and M. van Kreveld, "Computing longest duration flocks in trajectory data," in Proc. Annu. ACM Int. Symp. Geograph. Inf. Syst., 2006, pp. 35-42.
192. H. Jeung, M. L. Yiu, X. Zhou, C. S. Jensen, and H. T. Shen, "Discovery of convoys in trajectory databases," Proc. VLDB Endowment, vol. 1, no. 1, pp. 1068-1080, Aug. 2008.
193. L.-A. Tang et al., "On discovery of traveling companions from streaming trajectories," in Proc. IEEE Int. Conf. Data Eng., 2012, pp. 186-197.
194. K. Zheng, Y. Zheng, N. J. Yuan, and S. Shang, "On discovery of gathering patterns from trajectories," in Proc. IEEE ICDE, 2013, pp. 242-253.
195. Z. Li, B. Ding, J. Han, and R. Kays, "Swarm: Mining relaxed temporal moving object clusters," Proc. VLDB Endowment, vol. 3, no. 1/2, pp. 723-734, Sep. 2010.
196. C. S. Jensen, D. Lin, and B. C. Ooi, "Continuous clustering of moving objects," IEEE Trans. Knowl. Data Eng., vol. 19, no. 9, pp. 1161-1174, Sep. 2007.
197. Z. Li, J.-G. Lee, X. Li, and J. Han, "Incremental clustering for trajectories,"in Database Systems for Advanced Applications. Berlin, Germany: Springer-Verlag, 2010, pp. 32-46.
198. J.-G. Lee, J. Han, and K.-Y. Whang, "Trajectory clustering: A partition-and-group framework," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2007, pp. 593-604.
199. P. Yan, "Spatial-temporal data analytics and consumer shopping behavior modeling," Ph.D. dissertation, Univ. Arizona, Tucson, AZ, USA, 2010.
200. G. Gidofalvi and T. B. Pedersen, "Mining long, sharable patterns in trajectories of moving objects," Geoinformatica, vol. 13, no. 1, pp. 27-55, Mar. 2009.
201. F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, "Trajectory pattern mining," in Proc. ACM SIGKDD Int. Conf. Mining, 2007, pp. 330-339.
202. L.-Y. Wei, Y. Zheng, and W.-C. Peng, "Constructing popular routes from uncertain trajectories," in Proc. ACM SIGKDD Int. Conf. Mining, 2012, pp. 195-203.
203. P. Laube, S. Imfeld, and R. Weibel, "Discovering relative motion patterns in groups of moving point objects," Int. J. Geograph. Inf. Sci., vol. 19, no. 6, pp. 639-668, Jul. 2005.
204. Y. Zheng and X. Zhou, Computing With Spatial Trajectories. New York, NY, USA: Springer-Verlag, 2011.
205. A. Prasad Sistla, O. Wolfson, S. Chamberlain, and S. Dao, "Modeling and querying moving objects," in Proc. Int. Conf. Data Eng., 1997, pp. 422-432.
206. C. C. Aggarwal and D. Agrawal, "On nearest neighbor indexing of nonlinear trajectories," in Proc. ACM SIGMOD-SIGACT-SIGART Symp. Principles Database Syst., 2003, pp. 252-259.
207. Y. Tao, C. Faloutsos, D. Papadias, and B. Liu, "Prediction and indexing of moving objects with unknown motion patterns," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2004, pp. 611-622.
208. Y. Cai and R. Ng, "Indexing spatio-temporal trajectories with Chebyshev polynomials," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2004, pp. 599-610.
209. Y. Tao, D. Papadias, J. Zhai, and Q. Li, "Venn sampling: A novel prediction technique for moving objects," in Proc. Int. Conf. Data Eng., 2005, pp. 680-691.
210. J. Krumm and E. Horvitz, "Predestination: Inferring destinations from partial trajectories," in Ubiquitous Computing. Berlin, Germany: Springer-Verlag, 2006, pp. 243-260.
211. S.-W. Kim et al., "Path prediction of moving objects on road networks through analyzing past trajectories," in Proc. Int. Conf. Knowl.-Based Intell. Inf. Eng. Syst., 2007, pp. 379-389.
212. D. Tiesyte and C. S. Jensen, "Similarity-based prediction of travel times for vehicles traveling on known routes," in Proc. ACM SIGSPATIAL Int. Conf. Adv. Geograph. Inf. Syst., 2008, p. 14.
213. R. Simmons, B. Browning, Y. Zhang, and V. Sadekar, "Learning to predict driver route and destination intent," in Proc. Int. IEEE Conf. Intell. Transp. Syst., 2006, pp. 127-132.
214. B. D. Ziebart, A. L. Maas, A. K. Dey, and J. A. Bagnell, "Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior,"in Proc. Int. Conf. Ubiquitous Comput., 2008, pp. 322-331.
215. L. Chen, M. Lv, and G. Chen, "A system for destination and future route prediction based on trajectory mining," Pervasive Mobile Comput., vol. 6, no. 6, pp. 657-676, Dec. 2010.
216. J.-G. Lee, J. Han, X. Li, and H. Cheng, "Mining discriminative patterns for classifying trajectories on road networks," IEEE Trans. Knowl. Data Eng., vol. 23, no. 5, pp. 713-726, May 2011.
217. W. Mathew, R. Raposo, and B. Martins, "Predicting future locations with hidden Markov models," in Proc. ACM Conf. Ubiquitous Comput., 2012, pp. 911-918.
218. A. Y. Xue et al., "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction," in Proc. IEEE Int. Conf. Data Eng., 2013, pp. 254-265.
219. E. M. Knorr, R. T. Ng, and V. Tucakov, "Distance-based outliers: Algorithms and applications," VLDB J., vol. 8, no. 3/4, pp. 237-253, Feb. 2000.
220. J.-G. Lee, J. Han, and X. Li, "Trajectory outlier detection: A partition-and-detect framework," in Proc. IEEE Int. Conf. Data Eng., 2008, pp. 140-149.
221. X. Li, J. Han, S. Kim, and H. Gonzalez, "ROAM: Rule-and motif-based anomaly detection in massive moving object data sets," in Proc. SIAM Int. Conf. Data Mining, 2007, vol. 7, pp. 273-284.
222. X. Li, Z. Li, J. Han, and J.-G. Lee, "Temporal outlier detection in vehicle traffic data," in Proc. IEEE Int. Conf. Data Eng., 2009, pp. 1319-1322.
223. Y. Bu, L. Chen, A. W.-C. Fu, and D. Liu, "Efficient anomaly monitoring over moving object trajectory streams," in Proc. ACM SIGKDD Int. Conf. Mining, 2009, pp. 159-168.
224. Y. Ge et al., "Top-eye: Top-k evolving trajectory outlier detection," in Proc. ACM Int. Conf. Inf. Knowl. Manage., 2010, pp. 1733-1736.
225. D. Zhang et al., "iBAT: Detecting anomalous taxi trajectories from gps traces," in Proc. Int. Conf. Ubiquitous Comput., 2011, pp. 99-108.
226. L. Sun et al., "Real time anomalous trajectory detection and analysis,"Mobile Netw. App., vol. 18, no. 3, pp. 341-356, Jun. 2013.
227. C. Chen et al., "Real-time detection of anomalous taxi trajectories from GPS traces," in Mobile and Ubiquitous Systems: Computing, Networking, and Services. Berlin, Germany: Springer-Verlag, 2012, pp. 63-74.
228. L. X. Pang, S. Chawla, W. Liu, and Y. Zheng, "On detection of emerging anomalous traffic patterns using gps data," Data Knowl. Eng., vol. 87, pp. 357-373, Sep. 2013.
229. B. Krogh, O. Andersen, and K. Torp, "Trajectories for novel and detailed traffic information," in Proc. ACM SIGSPATIAL Int. Workshop GeoStreaming, 2012, pp. 32-39.
230. Z. Wang, M. Lu, X. Yuan, J. Zhang, and H. V. D. Wetering, "Visual traffic jam analysis based on trajectory data," IEEE Trans. Vis. Comput. Graphics, vol. 19, no. 12, pp. 2159-2168, Dec. 2013.
231. L. O. Alvares, V. Bogorny, J. A. F. de Macedo, B. Moelans, and S. Spaccapietra, "Dynamic modeling of trajectory patterns using data mining and reverse engineering," in Proc. Int. Conf. Conceptual Modeling, 2007, vol. 83, pp. 149-154.
232. A. T. Palma, V. Bogorny, B. Kuijpers, and L. O. Alvares, "A clustering-based approach for discovering interesting places in trajectories," in Proc. ACM Symp. Appl. Comput., 2008, pp. 863-868.
233. Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma, "Mining interesting locations and travel sequences from gps trajectories," in Proc. Int. Conf. World Wide Web, 2009, pp. 791-800.
234. X. Li, J. Han, J.-G. Lee, and H. Gonzalez, "Traffic density-based discovery of hot routes in road networks," in Advances in Spatial and Temporal Databases. Berlin, Germany: Springer-Verlag, 2007, pp. 441-459.
235. Q. Li, Z. Zeng, B. Yang, and T. Zhang, "Hierarchical route planning based on taxi gps-trajectories," in Proc. Int. Conf. Geoinformatics, 2009, pp. 1-5.
236. S. Oh et al., "A large-scale benchmark dataset for event recognition in surveillance video," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 3153-3160.
237. i-LIDS, Imagery Library for Intelligent Detection Systems. [Online]. Available: http://www.ilids.co.uk
238. Autoscope. [Online]. Available: http://www.autoscope.com
239. Citilog, Bala Cynwyd, PA, USA. [Online]. Available: http://www.citilog.com
240. Kapsch TrafficCom, Vienna, Austria. [Online]. Available: http://www.kapsch.net/en/ktc
241. Traficon. [Online]. Available: http://www.traficon.com
242. HIKVISION. [Online]. Available: http://www.hikvision.com/en/us
243. DAHUA, Hangzhou, China. [Online]. Available: http://www.dahuasecurity.com
244. eris. [Online]. Available: http://www.iteris.com
245. SONY. [Online]. Available: http://www.sony.net
246. On Semiconductor, Phoenix, AZ, USA. [Online]. Available: http://www.onsemi.com
247. N. K. Kanhere, S. J. Pundlik, and S. T. Birchfield, "Vehicle segmentation and tracking from a low-angle off-axis camera," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2005, vol. 2, pp. 1152-1157.
248. X. Wang, T. X. Han, and S. Yan, "An HOG-LBP human detector with partial occlusion handling," in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 32-39.
249. G. Shu, A. Dehghan, O. Oreifej, E. Hand, and M. Shah, "Part-based multiple-person tracking with partial occlusion handling," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2012, pp. 1815-1821.
250. S. Tang, M. Andriluka, and B. Schiele, "Detection and tracking of occluded people," in Proc. Brit. Mach. Vis. Conf., 2012, pp. 1-11.
251. K. Minoura and T. Watanabe, "Driving support by estimating vehicle behavior," in Proc. Int. Conf. Pattern Recog., Nov. 2012, pp. 1144-1147.
252. th World Congr. Intell. Transp. Syst., Oct. 2002, pp. 1-11.
253. B. T. Morris, C. Tran, G. Scora, M. M. Trivedi, and M. J. Barth, "Real-time video-based traffc measurement and visualization system for energy/emissions," IEEE Trans. Intell. Transp. Syst., vol. 13, no. 4, pp. 1667-1678, Dec. 2012.