Advertisement

Cost-Based Feature Transfer for Vehicle Occupant Classification

  • Toby PerrettEmail author
  • Majid Mirmehdi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

Abstract

Knowledge of human presence and interaction in a vehicle is of growing interest to vehicle manufacturers for design and safety purposes. We present a framework to perform the tasks of occupant detection and occupant classification for automatic child locks and airbag suppression. It operates for all passenger seats using a single overhead camera. A transfer learning technique is introduced to make full use of training data from all seats, whilst still maintaining some control over the bias necessary for a system designed to penalize certain misclassifications more than others. An evaluation is performed on a challenging dataset with both weighted and unweighted classifiers that demonstrates the effectiveness of the transfer process.

Keywords

Occupant Classification Transfer Learning Misclassification Cost Average Classification Accuracy Passenger Seat 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bottazzi, V.S., Borges, P.V.K., Jo, J.: A vision-based lane detection system combining appearance segmentation and tracking of salient points. In: IEEE Intelligent Vehicles Symposium, pp. 443–448 (2013)Google Scholar
  2. 2.
    Hanwell, D., Mirmehdi, M.: Detection of lane departure on high-speed roads. In: International Conference on Pattern Recognition Applications and Methods (2012)Google Scholar
  3. 3.
    Bonnin, S., Weisswange, T.H., Kummert, F., Schmuedderich, J.: Pedestrian crossing prediction using multiple context-based models. In: International Conference on Intelligent Transportation Systems (2014)Google Scholar
  4. 4.
    Monwar, M.M., Vijaya Kumar, B.V.K.: Vision-based potential collision detection for reversing vehicle. In: IEEE Intelligent Vehicles Symposium, pp. 88–93 (2013)Google Scholar
  5. 5.
    Sivaraman, S., Trivedi, M.M.: Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans. Intell. Transp. Syst. 14, 1773–1795 (2013)CrossRefGoogle Scholar
  6. 6.
    Vicente, F., Huang, Z., Xiong, X., Torre, F., Zhang, W., Levi, D.: Driver gaze tracking and eyes off the road detection system. IEEE Trans. Intell. Transp. Syst. 16, 1–14 (2015)CrossRefGoogle Scholar
  7. 7.
    Garcia, I., Bronte, S., Bergasa, L.M., Almazan, J., Yebes, J.: Vision-based drowsiness detector for real driving conditions. In: IEEE Intelligent Vehicles Symposium (2012)Google Scholar
  8. 8.
    Huang, S.S.: Discriminatively trained patch-based model for occupant classification. IET Intell. Transp. Syst. 6, 132–138 (2012)CrossRefGoogle Scholar
  9. 9.
    Huang, S.S., Jian, E., Hsiao, P.: Occupant classification invariant to seat movement for smart airbag. In: IEEE International Conference on Vehicular Electronics and Safety (2011)Google Scholar
  10. 10.
    Gao, Z., Duan, L.: Vision detection of vehicle occupant classification with Legendre moments and support vector machine. In: IEEE International Congress on Image and Signal Processing (2010)Google Scholar
  11. 11.
    Goktuk, S.B., Rafii, A.: An occupant classification system eigen shapes or knowledge-based features. In: Computer Vision and Pattern Recognition (2005)Google Scholar
  12. 12.
    Glass, R.J., Segui-Gomez, M., Graham, J.D.: Child passenger safety: decisions about seating location, airbag exposure, and restraint use. Risk Anal. 20, 521–527 (2000)CrossRefGoogle Scholar
  13. 13.
    Technologies, Challenges, and Research and Development Expenditures for Advanced Air Bags. Report to the Chairman and Ranking Minority Member, Committee on Commerce, Science, and Transportation, U.S. Senate (2001)Google Scholar
  14. 14.
    Mehney, M.A., McCarthy, M.C., Fullerton, M.G., Malecke, F.J.: Vehicle occupant weight sensor apparatus (2000)Google Scholar
  15. 15.
    Seip, R.: Linear ultrasound transducer array for an automotive occupancy sensor system (2002)Google Scholar
  16. 16.
    George, B., Zangl, H., Bretterklieber, T., Brasseur, G.: A combined inductive capacitive proximity sensor for seat occupancy detection. IEEE Trans. Instrum. Meas. 59, 1463–1470 (2010)CrossRefGoogle Scholar
  17. 17.
    Wallace, M.W.: Vehicle occupant classification system and method (2003)Google Scholar
  18. 18.
    Cheng, S.Y., Trivedi, M.M.: Vision-based infotainment user determination by hand recognition for driver assistance. IEEE Trans. Intell. Transp. Syst. 11, 759–764 (2010)CrossRefGoogle Scholar
  19. 19.
    Kong, H., Sun, Q., Bauson, W., Kiselewich, S., Ainslie, P., Hammoud, R.: Disparity based image segmentation for occupant classification. In: Computer Vision and Pattern Recognition Workshop (2004)Google Scholar
  20. 20.
    Cheng, S.Y., Trivedi, M.M.: Human posture estimation using voxel data for “smart” airbag systems: issues and framework. In: IEEE Intelligent Vehicles Symposium (2004)Google Scholar
  21. 21.
    Alefs, B., Clabian, M., Painter, M.: Occupant classification by boosting and PMD-technology. In: IEEE Intelligent Vehicles Symposium (2008)Google Scholar
  22. 22.
    Farmer, M.E., Jain, A.K.: Occupant classification system for automotive airbag suppression. In: Computer Vision and Pattern Recognition (2003)Google Scholar
  23. 23.
    Zhang, Y., Kiselewich, S.J., Bauson, W.A.: A monocular vision-based occupant classification approach for smart airbag deployment. In: IEEE Intelligent Vehicles Symposium (2005)Google Scholar
  24. 24.
    Devarakota, P.R.: Occupant classification using range images. IEEE Trans. Veh. Technol. 56, 1983–1993 (2007)CrossRefGoogle Scholar
  25. 25.
    Wang, S.: A new transfer learning boosting approach based on distribution measure with an application on facial expression recognition. In: International Joint Conference on Neural Networks (2014)Google Scholar
  26. 26.
    Shao, H., Tong, B., Suzuki, E.: Extended MDL principle for feature-based inductive transfer learning. Knowl. Inf. Syst. 35, 365–389 (2012)CrossRefGoogle Scholar
  27. 27.
    Farajidavar, N.: Adaptive transductive transfer machines. In: British Machine Vision Conference (2014)Google Scholar
  28. 28.
    Campos, T., Khan, A., Yan, F., Farajidavar, N., Windridge, D., Kittler, J., Christmas, W.: A framework for automatic sports video annotation with anomaly detection and transfer learning. In: Machine Learning and Cognitive Science (2013)Google Scholar
  29. 29.
    Rohrbach, M., Ebert, S., Schiele, B.: Transfer learning in a transductive setting. In: Neural Information Processing Systems (2013)Google Scholar
  30. 30.
    Pan, Z., Li, Y., Zhang, M., Sun, C., Guo, K., Tang, X., Zhou, S.Z.: IEEE Virtual Reality Conference (2010)Google Scholar
  31. 31.
    Garcke, J., Vanck, T.: Importance weighted inductive transfer learning for regression. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014, Part I. LNCS (LNAI), vol. 8724, pp. 466–481. Springer, Heidelberg (2014). doi: 10.1007/978-3-662-44848-9_30 Google Scholar
  32. 32.
    Xu, J., Ramos, S., Vazquez, D., Lopez, A.M.: Cost-sensitive structured SVM for multi-category domain adaptation. In: International Conference on Pattern Recognition (2014)Google Scholar
  33. 33.
    Farmer, M.E., Jain, A.K.: Smart automotive airbags: occupant classification and tracking. IEEE Trans. Veh. Technol. 56, 60–80 (2007)CrossRefGoogle Scholar
  34. 34.
    Scaramuzza, D., Martinelli, A., Siegwart, R.: A toolbox for easily calibrating omnidirectional cameras. In: IEEE International Conference on Intelligent Robots and Systems (2006)Google Scholar
  35. 35.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-24673-2_3 CrossRefGoogle Scholar
  36. 36.
    Gretton, A.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012)MathSciNetzbMATHGoogle Scholar
  37. 37.
    Kim, B., Pineau, J.: Maximum mean discrepancy imitation learning. In: Robotics: Science and Systems (2013)Google Scholar
  38. 38.
    Pan, S.J., Kwok, J.T., Yang, Q.: Transfer learning via dimensionality reduction. In: AAAI Conference on Artificial Intelligence (2008)Google Scholar
  39. 39.
    Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: International Conference on Computer Vision (2013)Google Scholar
  40. 40.
    Lagarias, J.C., Reeds, J., Wright, M.H., Wright, P.E.: Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J. Optim. 9, 112–147 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)CrossRefGoogle Scholar
  42. 42.
    Lin, H.: National Taiwan University, Technical report (2010)Google Scholar
  43. 43.
    Huang, S., Hsiao, P.Y.: Occupant classification for smart airbag using Bayesian filtering. In: International Conference on Green Circuits and Systems (2010)Google Scholar
  44. 44.
    Deng, J.D.J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: British Machine Vision Conference (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Visual Information LaboratoryUniversity of BristolBristolUK

Personalised recommendations