Abstract
Driving pattern recognition based on driving status features (GPS, gear, and speed etc.) is of central importance in the development of intelligent transportation. While it is expensive and labor intensive to obtain a large amount of labeled driving data in real applications. It makes the driving pattern recognition particularly difficult for those domains without labeled data. In this paper, to tackle this challenging recognition task, we propose a novel and robust Transfer Learning method for Driving Pattern Recognition (TLDPR) that can transfer knowledge from other related source domains with labeled data to the target domain. Compared to the traditional supervised learning, one of the major difficulties of transfer learning is that the data from different domains may have distinct distributions. The proposed TLDPR is able to reduce the distribution difference in RKHS between the samples in target and source domain with the same driving pattern, and it can preserve the local manifold structure simultaneously. In addition, an iterative ensemble strategy is implemented to make the model more robust using the pseudo-labels. To evaluate the performance of TLDPR, comprehensive experiments have been conducted on parking lots datasets. The results show TLDPR can substantially outperform the state-of-the-art methods.
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References
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer joint matching for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410–1417 (2014)
Gong, B., Shi, Y., Sha, F., Grauman K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2066–2073. IEEE (2012)
Sun, B., Feng, J., Saenko, K.: Return of frustratingly easy domain adaptation. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Aljundi, R., Emonet, R., Muselet, D., Sebban, M.: Landmarks-based kernelized subspace alignment for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 56–63 (2015)
Elhamifar, E., Vidal, R.: Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765–2781 (2013)
Wang, J., Chen, Y., Hu, L., Peng, X., Yu, P.S.: Stratified transfer learning for cross-domain activity recognition. In: 2018 IEEE International Conference on Pervasive Computing and Communications, pp. 1–10. IEEE (2018)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2200–2207 (2013)
Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: 2017 IEEE International Conference on Data Mining, pp. 1129–1134. IEEE (2017)
Long, M., Wang, J., Ding, G., Pan, S.J., Yu, P.S.: Adaptation regularization: a general framework for transfer learning. IEEE Trans. Knowl. Data Eng. 26(5), 1076–1089 (2014)
Wang, J., Feng, W., Chen, Y., Yu, H., Huang, M., Yu, P.S.: Visual domain adaptation with manifold embedded distribution alignment. In: 2018 ACM Multimedia Conference on Multimedia Conference, pp. 402–410. ACM (2018)
Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1859–1867 (2017)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7(Nov), 2399–2434 (2006)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, pp. 321–328 (2004)
Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1548–1560 (2011)
Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems, pp. 1601–1608 (2005)
Acknowledgments
This work is supported by the NSFC under Grant No. 61732011, 61702358, the Beijing Natural Science Foundation under Grant No. Z180006, and Key Scientific and Technological Support Projects of Tianjin Key R&D Program under Grant No. 18YFZCGX00390.
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Li, M., Yang, L., Hu, Q., Shen, C., Du, Z. (2019). Transfer Learning for Driving Pattern Recognition. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_5
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