Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

IEEE Transactions on Intelligent Transportation Systems

Volumn 16, Issue 4, 2015, Pages 2247-2256

  Dong, Zhen ^a   Wu, Yuwei ^a   Pei, Mingtao ^a   Jia, Yunde ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Feature learning; filter learning; multitask learning; neural network; Vehicle type classification

Indexed keywords

AMPHIBIOUS VEHICLES; COMPLEX NETWORKS; CONVOLUTION; NETWORK LAYERS; NEURAL NETWORKS; VEHICLES;

CLASSIFICATION TASKS; CONVOLUTIONAL NEURAL NETWORK; FEATURE LEARNING; FILTER LEARNING; LAPLACIAN FILTERS; MULTITASK LEARNING; SEMI-SUPERVISED; VEHICLE TYPES;

CLASSIFICATION (OF INFORMATION);

EID: 85028222906     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2015.2402438     Document Type: Article

References (42)

1. A. H. Lai, G. S. Fung, and N. H. Yung, "Vehicle type classification from visual-based dimension estimation, " in Proc. IEEE Intell. Transp. Syst. Conf., 2001, pp. 201-206.
2. S. Gupte, O. Masoud, R. F. Martin, and N. P. Papanikolopoulos, "Detection and classification of vehicles, " IEEE Trans. Intell. Transp. Syst., vol. 3, no. 1, pp. 37-47, Mar. 2002.
3. J.-W. Hsieh, S.-H. Yu, Y.-S. Chen, and W.-F. Hu, "Automatic traffic surveillance system for vehicle tracking and classification, " IEEE Trans. Intell. Transp. Syst., vol. 7, no. 2, pp. 175-187, Jun. 2006.
4. Z. Zhang, T. Tan, K. Huang, and Y. Wang, "Three-dimensional deformable-model-based localization and recognition of road vehicles, " IEEE Trans. Image Process., vol. 21, no. 1, pp. 1-13, Jan. 2012.
5. 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.
6. C. Zhang, X. Chen, andW.-B. Chen, "A PCA-based vehicle classification framework, " in Proc. IEEE 22nd Int. Conf. Data Eng. Workshops, 2006, pp. 17-17.
7. P. Ji, L. Jin, and X. Li, "Vision-based vehicle type classification using partial Gabor filter bank, " in Proc. IEEE Int. Conf. Autom. Logist., 2007, pp. 1037-1040.
8. Y. Shan, H. S. Sawhney, and R. Kumar, "Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 4, pp. 700-711, Apr. 2008.
9. M. Jiang and H. Li, "Vehicle classification based on hierarchical support vector machine, " in Computer Engineering and Networking, Dordrecht, Switzerland: Springer-Verlag, 2014, pp. 593-600.
10. D. G. Lowe, "Distinctive image features from scale-invariant keypoints, " Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, Jan. 2004.
11. I. Sobel, "Camera models and machine perception, " Stanford Univ. Artif. Intell. Lab, Stanford, CA, USA, Tech. Rep. AIM-21, 1970.
12. 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.
13. P. Negri, X. Clady, M. Milgram, and R. Poulenard, "An oriented-contour point based voting algorithm for vehicle type classification, " in Proc. IEEE Int. Conf. Pattern Recog., 2006, vol. 1, pp. 574-577.
14. A. Psyllos, C.-N. Anagnostopoulos, and E. Kayafas, "Vehicle model recognition from frontal view image measurements, " Comput. Std. Interfaces, vol. 33, no. 2, pp. 142-151, Jun. 2011.
15. 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.
16. Y. Peng et al., "Vehicle type classification using data mining techniques, " in The Era of Interactive Media, New York, NY, USA: Springer-Verlag, 2013, pp. 325-335.
17. 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.
18. C. Garcia and M. Delakis, "A neural architecture for fast and robust face detection, " in Proc. IEEE 16th Int. Conf. Pattern Recog., 2002, vol. 2, pp. 44-47.
19. M. Osadchy, Y. L. Cun, and M. L. Miller, "Synergistic face detection and pose estimation with energy-based models, " J. Mach. Learn. Res., vol. 8, pp. 1197-1215, 2007.
20. Y. Sun, X. Wang, and X. Tang, "Deep convolutional network cascade for facial point detection, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2013, pp. 3476-3483.
21. P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun, "Pedestrian detection with unsupervised multi-stage feature learning, " in Proc. IEEE CVPR, 2013, pp. 3626-3633.
22. N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev, "PANDA: Pose aligned networks for deep attribute modeling, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2014, pp. 1637-1644.
23. L. Kang, P. Ye, Y. Li, and D. Doermann, "Convolutional neural networks for no-reference image quality assessment, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2014, pp. 1733-1740.
24. A. Krizhevsky, I. Sutskever, and G. Hinton, "ImageNet classification with deep convolutional neural networks, " in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1106-1114.
25. A. Karpathy et al., "Large-scale video classification with convolutional neural networks, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2014, pp. 1725-1732.
26. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks, " Science, vol. 313, no. 5786, pp. 504-507, Jul. 2006.
27. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, "Greedy layerwise training of deep networks, " in Proc. Adv. Neural Inf. Process. Syst., 2007, vol. 19, pp. 153-160.
28. K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun, "What is the best multi-stage architecture for object recognition?" in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 2146-2153.
29. K. Kavukcuoglu et al., "Learning convolutional feature hierarchies for visual recognition, " in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 1090-1098.
30. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold regularization: A geometric framework for learning from labeled and unlabeled examples, " J. Mach. Learn. Res., vol. 7, pp. 2399-2434, 2006.
31. A. Kumar and H. Daume III, "Learning task grouping and overlap in multi-task learning, " in Proc. Int. Conf. Mach. Learn., 2012, pp. 1383-1390.
32. D. H. Hubel and T. N.Wiesel, "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, " J. Physiol., vol. 160, no. 1, pp. 106-154, Jan. 1962.
33. K. Fukushima and S. Miyake, "Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position, " Pattern Recognit., vol. 15, no. 6, pp. 455-469, 1982.
34. S. Lyu and E. P. Simoncelli, "Nonlinear image representation using divisive normalization, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2008, pp. 1-8.
35. N. Pinto, D. D. Cox, and J. J. DiCarlo, "Why is real-world visual object recognition hard?" PLoS Comput. Biol., vol. 4, no. 1, p. e27, Jan. 2008.
36. J. Ngiam, Z. Chen, S. A. Bhaskar, P. W. Koh, and A. Ng, "Sparse filtering, " in Proc. Adv. Neural Inf. Process. Syst., 2011, pp. 1125-1133.
37. D. J. Field, "What is the goal of sensory coding?" Neural Comput., vol. 6, no. 4, pp. 559-601, Jul. 1994.
38. B. Willmore and D. J. Tolhurst, "Characterizing the sparseness of neural codes, " Netw., Comput. Neural Syst., vol. 12, no. 3, pp. 255-270, Aug. 2001.
39. J. Nocedal, "Updating quasi-Newton matrices with limited storage, " Math. Comput., vol. 35, no. 151, pp. 773-782, Jul. 1980.
40. A. Beck and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse problems, " SIAM J. Imag. Sci., vol. 2, no. 1, pp. 183-202, Jan. 2009.
41. Y. Peng, J. S. Jin, S. Luo, M. Xu, and Y. Cui, "Vehicle type classification using PCA with self-clustering, " in Proc. IEEE Int. Conf. Multimedia Expo Workshops, 2012, pp. 384-389.
42. D. Zhen and Y. Jia, "Vehicle type classification using distributions of structural and appearance-based features, " in Proc. Int. Conf. Image Process., 2013, pp. 4321-4324.