Domain adaptation of deformable part-based models

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 36, Issue 12, 2014, Pages 2367-2380

  Xu, Jiaolong ^a   Ramos, Sebastian ^a   Vazquez, David ^a   Lopez, Antonio M ^a  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

Author keywords

deformable part based model; Domain adaptation; pedestrian detection

Indexed keywords

DEFORMABLE PART-BASED MODELS; DOMAIN ADAPTATION; PEDESTRIAN DETECTION;

HUMAN; IMAGE PROCESSING; MACHINE LEARNING; PROCEDURES; SUPPORT VECTOR MACHINE; THEORETICAL MODEL; VIDEORECORDING; WALKING;

HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MODELS, THEORETICAL; SUPPORT VECTOR MACHINE; VIDEO RECORDING; WALKING;

EID: 84908673598     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2014.2327973     Document Type: Article

References (47)

1. D. Vazquez, A. Lopez, J. Marin, D. Ponsa, and D. Geronimo, "Virtual and real world adaptation for pedestrian detection", IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 4, pp. 797-809, Apr. 2014.
2. A. Torralba, and A. A. Efros, "Unbiased look at dataset bias", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Colorado Springs, CO, USA, 2011, pp. 1521-1528.
3. J. Quinonero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, Eds., Dataset shift in Machine Learning (Neural Information Processing Series). Cambridge, MA, USA: MIT Press, 2008.
4. P. Felzenszwalb, R. 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.
5. C.-N. J. Yu and T. Joachims, "Learning structural SVMs with latent variables", in Proc. 26th Annu. Int. Conf. Mach. Learn., Montreal, Quebec, 2009, pp. 1169-1176.
6. A. Vedaldi and A. Zisserman, "Structured output regression for detection with partial truncation", in Proc. Adv. Neural Inf. Process. Syst., Vancouver, Canada, 2009, pp. 1928-1936.
7. L. Zhu, Y. Chen, A. Yuille, and W. Freeman, "Latent hierarchical structural learning for object detection", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., San Francisco, CA, USA, 2010, pp. 1062-1069.
8. J. Yang, R. Yan, and A. Hauptmann, "Cross-domain video concept detection using adaptive SVMs", in Proc. ACM 15th Int. Conf. Multimedia, Augsburg, Germany, 2007, pp. 188-197.
9. M. Kumar, B. Packer, and D. Koller, "Self-paced learning for latent variable models", in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, 2010, pp. 1189-1197.
10. M. Enzweiler and D. M. Gavrila, "Monocular pedestrian detection: Survey and experiments", IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 12, pp. 2179-2195, Dec. 2009.
11. D. Geronimo, A. M. Lopez, A. D. Sappa, and T. Graf, "Survey of pedestrian detection for advanced driver assistance systems", IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 7, pp. 1239-1258, Jul. 2010.
12. P. Dollar, C. Wojek, B. Schiele, and P. Perona, "Pedestrian detection: An evaluation of the state of the art", IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 743-761, Apr. 2012.
13. X. Cao, Z. Wang, P. Yan, and X. Li, "Transfer learning for pedestrian detection", Neurocomput., vol. 100, pp. 51-57, 2013.
14. J. Pang, Q. Huang, S. Yan, S. Jiang, and L. Qin, "Transferring boosted detectors towards viewpoint and scene adaptiveness", IEEE Trans. Image Process., vol. 20, no. 5, pp. 1388-400, May 2011.
15. D. Vazquez, A. Lopez, D. Ponsa, and D. Geronimo, "Interactive training of human detectors", Multimodal Interaction Image Video Appl., vol. 48, pp. 169-184, 2013.
16. D. Vazquez, A. Lopez, and D. Ponsa, "Unsupervised domain adaptation of virtual and real worlds for pedestrian detection", in Proc. 21st Int. Conf. Pattern Recognit., Tsukuba, Japan, 2012, pp. 3492-3495.
17. M. Wang and X. Wang, "Automatic adaptation of a generic pedestrian detector to a specific traffic scene", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Colorado Springs, CO, USA, 2011, pp. 3401-3408.
18. M. Wang and X. Wang, "Transferring a generic pedestrian detector towards specific scenes", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Providence, RI, USA, 2012, pp. 3274-3281.
19. J. Xu, D. Vazquez, S. Ramos, A. Lopez, and D. Ponsa, "Adapting a pedestrian detector by boosting LDA exemplar classifiers", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Portland, OR, USA, 2013, pp. 688-693.
20. J. Donahue, J. Hoffman, E. Rodner, K. Saenko, and T. Darrell, "Semi-supervised domain adaptation with instance constraints", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Portland, OR, USA, 2013, pp. 668-675.
21. A. Bergamo, and L. Torresani, "Exploring weakly-labeled web images to improve object classification: A domain adaptation approach", in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, 2010, pp. 181-189.
22. A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari, "Learning object class detectors from weakly annotated video", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Providence, RI, USA, 2012, pp. 3282-3289.
23. H. D. III, "Frustratingly easy domain adaptation", in Proc. Annu. Meet. Assoc. Comput. Linguistics, Prague, Czech Republic, 2007, pp. 256-263.
24. W. Jiang, E. Zavesky, C. Shih-Fu, and A. Loui, "Cross-domain learning methods for high-level visual concept classification", in Proc. IEEE Int. Conf. Image Process., San Diego, CA, USA, 2008, pp. 161-164.
25. F. Orabona, C. Castellini, B. Caputo, A. Fiorilla, and G. Sandini, "Model adaptation with least-squares SVM for adaptive hand prosthetics", in Proc. IEEE Int. Conf. Robot. Autom., Kobe, Japan, 2009, pp. 2897-2903.
26. Y. Aytar and A. Zisserman, "Tabula rasa: Model transfer for object category detection", in Proc. Int. Conf. Comput. Vis., 2011, pp. 2252-2259.
27. L. Duan, I. Tsang, D. Xu, and S. Maybank, "Domain transfer SVM for video concept detection", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Miami Beach, FL, USA, 2009, pp. 1375-1381.
28. R. Gopalan, R. Li, and R. Chellappa, "Domain adaptation for object recognition: An unsupervised approach", in Proc. IEEE Int. Conf. Comput. Vis., Barcelona, Spain, 2011, pp. 999-1006.
29. B. Gong, Y. Shi, F. Sha, and K. Grauman, "Geodesic flow kernel for unsupervised domain adaptation", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Providence, RI, USA, 2012, pp. 2066-2073.
30. F. Mirrashed, V. Morariu, S. Behjat, R. Feris, and L. Davis, "Domain adaptive object detection", in Proc. IEEE Workshop Appl. Comput. Vis., Washington, DC, USA, 2013, pp. 323-330.
31. S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, "Domain adaptation via transfer component analysis", in Proc. 21st Int. Joint Conf. Artif. Intell., Pasadena, California, USA, 2009, pp. 1187-1192.
32. V. Jain and E. Learned-Miller, "Online domain adaptation of a pre-trained cascade of classifiers", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Colorado Springs, CO, USA, 2011, pp. 577-584.
33. K. Saenko, B. Hulis, M. Fritz, and T. Darrel, "Adapting visual category models to new domains", in Proc. 11th Eur. Conf. Comput. Vis., Hersonissos, Heraklion, Crete, Greece, 2010, pp. 213-226.
34. R. Girshick, "From rigid templates to grammars: Object detection with structured models", Ph. D. dissertation, Dept. Comput. Sci., the Univ. Chicago, Chicago, IL, USA, 2012.
35. C. N. Yu, "Improved learning of structural support vector machines: Training with latent variables and non-linear kernels", Ph. D. dissertation, Cornell Univ., Ithaca, NY, USA, 2011.
36. Y. Aytar and A. Zisserman, "Enhancing exemplar SVMs using part level transfer regularization", in Proc. Brit. Mach. Vis. Conf., Surrey, U. K., 2012, pp. 1-11.
37. J. Blitzer, R. McDonald, and F. Pereira, "Domain adaptation with structural correspondence learning", in Proc. Int. Conf. Empirical Methods Natural Language Process., Sydney, Australia, 2006, pp. 120-128.
38. K. Tang, V. Ramanathan, F.-F. Li, and D. Koller, "Shifting weights: Adapting object detectors from image to video", in Proc. Adv. Neural Inf. Process. Syst., Lake Tahoe, NV, USA, 2012, pp. 647-655.
39. C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning. Cambridge, MA, USA: MIT Press, 2006.
40. R. Girshick, P. Felzenszwalb, and D. McAllester, Discriminatively trained deformable part models, release 5. (2010) [Online]. Available: http://people.cs.uchicago.edu/rbg/latent-release5
41. F. Wilcoxon, "Individual comparisons by ranking methods", Biometrics Bull., vol. 1, no. 6, pp. 80-83, 1945.
42. M. Schmidt, Minconf - Projection methods for optimization with simple constraints in matlab. (2008) [Online]. Available: http:/www.di.ens.fr/mschmidt/Software/minConf.html
43. J. Marin, D. Vazquez, D. Geronimo, and A. M. Lopez, "Learning appearance in virtual scenarios for pedestrian detection", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., San Francisco, CA, USA, 2010, pp. 137-144.
44. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., San Diego, CA, USA, 2005, pp. 886-893.
45. C. Wojek, S. Walk, and B. Schiele, "Multi-cue onboard pedestrian detection", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Miami Beach, FL, USA, 2009, pp. 794-801.
46. A. Geiger, C. Wojek, and R. Urtasun, "Joint 3D estimation of objects and scene layout", in Proc. Adv. Neural Inf. Process. Syst., Granada, Spain, 2011, pp. 1467-1475.
47. D. Geronimo, A. Sappa, D. Ponsa, and A. Lopez, "2D-3D based on-board pedestrian detection system", Comput. Vis. Image Understanding, vol. 114, no. 5, pp. 583-595, 2010.