Vehicle detection in aerial imagery: A small target detection benchmark

Journal of Visual Communication and Image Representation

Volumn 34, Issue , 2016, Pages 187-203

  Razakarivony, Sebastien ^a,b   Jurie, Frederic ^b  

^a SAFRAN AIRCRAFT ENGINES   (France)

^b UNIVERSITÉ DE CAEN   (France)

Author keywords

Aerial imagery; Computer vision; Database; Detection; Infrared imagery; Low resolution images; Target detection; Vehicles

Indexed keywords

AERIAL PHOTOGRAPHY; ALGORITHMS; AUTOMATIC TARGET RECOGNITION; COMPUTER VISION; DATABASE SYSTEMS; ERROR DETECTION; OBJECT DETECTION; OBJECT RECOGNITION; TARGET TRACKING; VEHICLES;

AERIAL IMAGERY; EXPERIMENTAL PROTOCOLS; INFRARED IMAGERY; LOW RESOLUTION IMAGES; MULTIPLE ORIENTATIONS; SMALL TARGET DETECTION; TARGET RECOGNITION ALGORITHMS; UNCONSTRAINED ENVIRONMENTS;

IMAGE PROCESSING;

EID: 84948957848     PISSN: 10473203     EISSN: 10959076     Source Type: Journal    
DOI: 10.1016/j.jvcir.2015.11.002     Document Type: Article

References (87)

1. T. Wong, Atr applications in military missions, in: IEEE Symposium on Computational Intelligence in Security and Defense Applications, 2007, pp. 30-32.
2. B. Bhanu Automatic target recognition: state of the art survey IEEE Trans. Aerosp. Electron. Syst. 22 1986 364 379
3. P. Viola, M.J. Jones, Robust real time face detection, 2004, pp. 137-154.
4. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 886-893.
5. G. Csurka, C. Dance, L. Fan, J. Willamowski, C. Bray, Visual categorization with bags of keypoints, in: European Conference on Computer Vision, 2004, p. 22.
6. H. Harzallah, F. Jurie, C. Schmid, Combining efficient object localization and image classification, in: International Conference on Computer Vision, 2009, pp. 237-244.
7. P. Felzenszwalb, R. Girshick, D. Mcallester, and D. Ramanan Object detection with discriminatively trained part based models IEEE Trans. Pattern Anal. Mach. Intell. 32 2009 1627 1645
8. M. Everingham, L.V. Gool, C.K.I. Williams, J. Winn, and A. Zisserman The pascal VOC challenge Int. J. Comput. Vision 88 2010 303 338
9. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, Imagenet: a large-scale hierarchical image database, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255.
10. B.C. Russell, A. Torralba, K.P. Murphy, and W.T. Freeman Labelme: a database and web-based tool for image annotation Int. J. Comput. Vision 77 2008 157 173
11. Q.V. Le Building high-level features using large scale unsupervised learning ICASSP 2013 IEEE 8595 8598
12. S. Munder, An experiment study on pedestrian classification, vol. 28, 2006.
13. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner Gradient-based learning applied to document recognition Proc. IEEE 86 1998 2278 2324
14. A. Torralba, R. Fergus, and W.T. Freeman 80 million tiny images: a large data set for nonparametric object and scene recognition IEEE Trans. Pattern Anal. Mach. Intell. 30 2008 1958 1970
15. P.J. Phillips, H. Wechsler, J. Huang, and P.J. Rauss The FERET database and evaluation procedure for face recognition algorithms Image Vis. Comput. 16 1998 295 306
16. F.S. Samaria, A.C. Harter, Parameterisation of a stochastic model for human face identification, in: IEEE Workshop on Applications of Computer Vision, 1994, pp. 138-142.
17. C. Papageorgiou, and T. Poggio A trainable system for object detection Int. J. Comput. Vision 38 2000 15 33
18. P. Dollár, C. Wojek, B. Schiele, P. Perona, Pedestrian detection: a benchmark, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 304-311.
19. H.A. Rowley, S. Baluja, and T. Kanade Neural network-based face detection IEEE Trans. Pattern Anal. Mach. Intell. 20 1998 23 38
20. K.-K. Sung, and T. Poggio Example-based learning for view-based human face detection IEEE Trans. Pattern Anal. Mach. Intell. 20 1998 39 51
21. E. Hjelmås, and B.K. Low Face detection: a survey Comput. Vision Image Understand. 83 2001 236 274
22. A. Loui, C. Judice, S.S. Liu, An image database for benchmarking of automatic face detection and recognition algorithms, in: ICIP, 1998, pp. 146-150
23. V. Jain, E. Learned-Miller, Fddb: a benchmark for face detection in unconstrained settings, Tech. Rep. UM-CS-2010-009, University of Massachusetts, Amherst, 2010.
24. T.L. Berg, A.C. Berg, J. Edwards, and D.A. Forsyth Who's in the picture Adv. Neural Inform. Process. Syst. 17 2005 137 144
25. V. Ferrari, T. Tuytelaars, L. Van Gool, Object detection by contour segment networks, in: European Conference on Computer Vision, 2006, pp. 14-28.
26. F. Tanner, B. Colder, C. Pullen, D. Heagy, M. Eppolito, V. Carlan, C. Oertel, P. Sallee, Overhead imagery research data set: an annotated data library and tools to aid in the development of computer vision algorithms, in: Proceedings of IEEE Applied Imagery Pattern Recognition Workshop, 2009, pp. 1-8.
27. A. Torralba, and A.A. Efros Unbiased look at dataset bias IEEE Conference on Computer Vision and Pattern Recognition 2011 IEEE 1521 1528
28. J. Ponce, T.L. Berg, M. Everingham, D.A. Forsyth, M. Hebert, S. Lazebnik, M. Marszalek, C. Schmid, B.C. Russell, A. Torralba, and et al. Dataset issues in object recognition Toward Category-level Object Recognition 2006 Springer 29 48
29. P. Carbonetto, G. Dorkó, C. Schmid, H. Kück, and N. De Freitas Learning to recognize objects with little supervision Int. J. Comput. Vision 77 2008 219 237
30. J. Gleason, A.V. Nefian, X. Bouyssounousse, T. Fong, G. Bebis, Vehicle detection from aerial imagery, in: IEEE International Conference on Robotics and Automation, 2011, pp. 2065-2070.
31. U. Stilla, E. Michaelsen, U. Soergel, S. Hinz, and H. Ender Airborne monitoring of vehicle activity in urban areas Int. Arch. Photogramm. Remote Sens. 35 2004 973 979
32. website, < http://www.ipf.kit.edu/english/downloads-707.php >.
33. A. Kembhavi, D. Harwood, and L.S. Davis Vehicle detection using partial least squares IEEE Trans. Pattern Anal. Mach. Intell. 33 2011 1250 1265
34. C. Diegert A combinatorial method for tracing objects using semantics of their shape 2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) 2010 IEEE 1 4
35. J. Ponce, M. Hebert, C. Schmid, and A. Zisserman Toward Category-Level Object Recognition 2006 Springer
36. J. Davis, M. Goadrich, The relationship between precision-recall and ROC curves, in: International Conference on Machine Learning, 2006, pp. 233-240.
37. L. Lusted Signal detectability and medical decision-making Science 1971 1217 1219
38. O. Tuzel, F. Porikli, P. Meer, Region covariance: a fast descriptor for detection and classification, in: European Conference on Computer Vision, 2006, pp. 589-600.
39. V. Raghavan, P. Bollmann, and G.S. Jung A critical investigation of recall and precision as measures of retrieval system performance ACM Trans. Inform. Syst. 1989 205 229
40. C. Gu, P.A. Arbelez, Y. Lin, K. Yu, J. Malik, Multi-component models for object detection, in: European Conference on Computer Vision, 2012, pp. 445-458.
41. K.E.A. van de Sande, J.R.R. Uijlings, T. Gevers, A.W.M. Smeulders, Segmentation as selective search for object recognition, in: International Conference on Computer Vision, 2011, pp. 1879-1886.
42. M. Pedersoli, A. Vedaldi, J. Gonzlez, A coarse-to-fine approach for fast deformable object detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 1353-1360.
43. P.F. Felzenszwalb, R.B. Girshick, D.A. McAllester, Cascade object detection with deformable part models, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 2241-2248.
44. C.D. Manning, P. Raghavan, and H. Schütze Introduction to Information Retrieval 2008 Cambridge University Press Cambridge
45. G. Liu, and R.M. Haralick Optimal matching problem in detection and recognition performance evaluation Pattern Recogn. 35 10 2002 2125 2139
46. X. Jiang, C. Marti, C. Irniger, and H. Bunke Distance measures for image segmentation evaluation EURASIP J. Appl. Signal Process. 2006 2006 1 10 Article ID 35909 10.1155/ASP/2006/35909
47. A. Hoover, G. Jean-Baptiste, X. Jiang, P.J. Flynn, H. Bunke, D.B. Goldgof, K. Bowyer, D.W. Eggert, A. Fitzgibbon, R.B. Fisher, An experimental comparison of range image segmentation algorithms, vol. 18, 1996, pp. 673-689.
48. V.Y. Mariano, J. Min, J.-H. Park, R. Kasturi, D. Mihalcik, H. Li, D. Doermann, T. Drayer, Performance evaluation of object detection algorithms, in: IAPR International Conference on Pattern Recognition, vol. 3, 2002, pp. 965-969.
49. D. Hoiem, Y. Chodpathumwan, Q. Dai, Diagnosing error in object detectors, in: European Conference on Computer Vision, 2012, pp. 340-353.
50. P. Dollár, Z. Tu, P. Perona, S. Belongie, Integral channel features, in: British Machine Vision Conference, 2009, p. 5.
51. B. Wu, R. Nevatia, Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors, in: International Conference on Computer Vision, 2005, pp. 90-97.
52. S. Maji, A. Berg, J. Malik, Classification using intersection kernel support vector machines is efficient, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
53. L. Zhu, Y. Chen, A.L. Yuille, W.T. Freeman, Latent hierarchical structural learning for object detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 1062-1069.
54. X. Wang, A discriminative deep model for pedestrian detection with occlusion handling, in: IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 3258-3265.
55. W. Ouyang, and X. Wang Joint deep learning for pedestrian detection International Conference on Computer Vision 2013 IEEE 2056 2063
56. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097-1105.
57. A. Sharif Razavian, H. Azizpour, J. Sullivan, S. Carlsson, Cnn features off-the-shelf: an astounding baseline for recognition, arXiv preprint arXiv: 1403.6382.
58. M. Oquab, L. Bottou, I. Laptev, J. Sivic, Learning and transferring mid-level image representations using convolutional neural networks, in: IEEE Conference on Computer Vision and Pattern Recognition, 2014.
59. P. Viola, M.J. Jones, and D. Snow Detecting pedestrians using patterns of motion and appearance Int. J. Comput. Vision 63 2005 153 161
60. F.M. Porikli, Integral histogram: a fast way to extract histograms in cartesian spaces, in: IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 829-836.
61. P. Sabzmeydani, G. Mori, Detecting pedestrians by learning shapelet features, in: IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1-8.
62. T. Malisiewicz, A. Gupta, A.A. Efros, Ensemble of exemplar-svms for object detection and beyond, in: International Conference on Computer Vision, 2011, pp. 89-96.
63. C.H. Lampert, M.B. Blaschko, T. Hofmann, Beyond sliding windows: object localization by efficient subwindow search, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008.
64. A. Bosch, A. Zisserman, X. Muoz, Representing shape with a spatial pyramid kernel, in: Conference on Image and Video Retrieval, 2007, pp. 401-408.
65. W.R. Schwartz, A. Kembhavi, D. Harwood, L.S. Davis, Human detection using partial least squares analysis, in: International Conference on Computer Vision, 2009, pp. 24-31.
66. X. Wang, T. Han, S. Yan, An hog-lbp human detector with partial occlusion handling, in: International Conference on Computer Vision, 2009, pp. 32-39.
67. S. Walk, N. Majer, K. Schindler, B. Schiele, New features and insights for pedestrian detection., in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 1030-1037.
68. M. Enzweiler, and D. Gavrila Monocular pedestrian detection: Survey and experiments IEEE Trans. Pattern Anal. Mach. Intell. 31 2009 2179 2195
69. P.F. Felzenszwalb, and D.P. Huttenlocher Pictorial structures for object recognition Int. J. Comput. Vision 61 2005 55 79
70. L.D. Bourdev, J. Malik, Poselets: body part detectors trained using 3d human pose annotations, in: International Conference on Computer Vision, 2009, pp. 1365-1372.
71. Y. Yang, D. Ramanan, Articulated pose estimation with flexible mixtures-of-parts, in: IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 1385-1392.
72. A. Vedaldi, V. Gulshan, M. Varma, A. Zisserman, Multiple kernels for object detection, in: International Conference on Computer Vision, 2009, pp. 606-613.
73. B. Alexe, T. Deselaers, V. Ferrari, What is an object?, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 73-80.
74. E. Rahtu, J. Kannala, M.B. Blaschko, Learning a category independent object detection cascade, in: International Conference on Computer Vision, 2011, pp. 1052-1059.
75. H.-Y. Cheng, C.-C. Weng, and Y.-Y. Chen Vehicle detection in aerial surveillance using dynamic bayesian networks IEEE Trans. Image Process. 21 4 2012 2152 2159
76. website, Utah agrc website, 2012. < http://gis.utah.gov/ >.
77. C.M. Bishop, and et al. Pattern recognition and machine learning vol. 1 2006 Springer New York
78. T. Ojala, M. Pietikainen, and T. Maenpaa Multiresolution gray-scale and rotation invariant texture classification with local binary patterns IEEE Trans. Pattern Anal. Mach. Intell. 24 2002 971 987
79. X. Tan, B. Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions, in: Analysis and Modeling of Faces and Gestures, 2007, pp. 168-182.
80. R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, 2009.
81. J. Gall, and V. Lempitsky Class-specific hough forests for object detection 2013 IEEE Conference on Computer Vision and Pattern Recognition 2009 IEEE 1022 1029
82. T. Joachims, Making large-scale support vector machine learning practical, 1999. < http://svmlight.joachims.org/ >.
83. L. Rousseeuw, L. Kaufman, Clustering by means of medoids, Statistical data analysis based on the L1-norm and related methods, pp. 405-416.
84. D. Park, D. Ramanan, C. Fowlkes, Multiresolution models for object detection, in: European Conference on Computer Vision, 2010, pp. 241-254.
85. J. Yan, X. Zhang, Z. Lei, S. Liao, S.Z. Li, Robust multi-resolution pedestrian detection in traffic scenes, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2013.
86. P. Dollar, C. Wojek, B. Schiele, P. Perona, Pedestrian detection: an evaluation of the state of the art, vol. 34, 2012, pp. 743-761.
87. S.K. Divvala, A.A. Efros, M. Hebert, How important are Ìdeformable partsÎ in the deformable parts model?, in: European Conference on Computer Vision, 2012, pp. 31-40.