Accuracy of Trajectories Estimation in a Driver-Assistance Context

  • Waqar Khan
  • Reinhard Klette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8334)

Abstract

Feature-point tracking for the purpose of object tracking in a driver-assistance context is not an easy task. First, to track rigid objects, feature points have to be matched frame-by-frame and then, by using disparity maps, their real-world position can be derived, from which the object velocity is estimated.

Unfortunately, a feature-point matcher cannot find (reliable) matches in all frames. In fact, the performance of a matcher varies with the type of feature-point detector and descriptor used. Our comparison of different feature-point matchers gives a general impression of how descriptor performance degrades as a rigid object approaches the ego-vehicle in a collision-scenario video sequence. To handle the mismatches, we use a Kalman-filter-based tracker for each tracked feature point. The tracker with the maximum number of matches and with a most recent match is chosen as the optimal tracker. The role of the optimal tracker is to assist in updating the tracker of a feature point which had no match. The optimal tracker is also used in estimating the object velocity.

To understand the behaviour of the safety system, we used the DoG detector in combination with SURF, BRIEF, and FREAK descriptors, while linBP and iSGM are used as stereo matchers. The novelty in our work is the performance evaluation of a stereo-based collision avoidance system (avoidance by brake warning) in a real collision scenario.

Keywords

Feature Point Rigid Object Driver Assistance System Timely Warning Trajectory Estimation 
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.
    Abe, G., Richardson, J.: The influence of alarm timing on driver response to collision warning systems following system failure. J. Behaviour & Information Technology 25(5), 443–452 (2006)CrossRefGoogle Scholar
  2. 2.
    Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: Fast retina keypoint. In: Proc. IEEE Int. Conf. Computer Vision Pattern Recognition, pp. 510–517 (2012)Google Scholar
  3. 3.
    Bay, H., Tuytelaars, T., Gool, L.V.: Surf: Speeded up robust features. In: Proc. European Conf. Computer Vision, pp. 408–417 (2006)Google Scholar
  4. 4.
    Botterill, T., Mills, S., Green, R.: Fast RANSAC hypothesis generation for essential matrix estimation. In: Proc. Int. Conf. Digital Image Computing Techniques Applications, pp. 561–566 (2011)Google Scholar
  5. 5.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: Binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Coifman, B., Beymer, D., McLauchlan, P., Malik, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C: Emerging Technologies 6(4), 271–288 (1998)CrossRefGoogle Scholar
  7. 7.
    Fischler, M.A., Bolles, C.R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Engineering 82(1), 35–45 (1960)CrossRefGoogle Scholar
  9. 9.
    Khan, W., Morris, J.: Safety of stereo driver assistance systems. In: Proc. IEEE Symp. Intell. Vehicles (IV), pp. 469–475 (2012)Google Scholar
  10. 10.
    Khan, W., Klette, R.: Stereo accuracy for collision avoidance for varying collision trajectories. In: Proc. IEEE Symp. Intell. Vehicles (IV) (2013)Google Scholar
  11. 11.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. IEEE Int. Conf. Computer Vision, pp. 1150–1157 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Waqar Khan
    • 1
  • Reinhard Klette
    • 1
  1. 1.Computer Science Department, Tamaki Innovation CampusThe University of AucklandNew Zealand

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