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.
Chapter PDF
Similar content being viewed by others
Keywords
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
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)
Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: Fast retina keypoint. In: Proc. IEEE Int. Conf. Computer Vision Pattern Recognition, pp. 510–517 (2012)
Bay, H., Tuytelaars, T., Gool, L.V.: Surf: Speeded up robust features. In: Proc. European Conf. Computer Vision, pp. 408–417 (2006)
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)
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)
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)
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)
Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Engineering 82(1), 35–45 (1960)
Khan, W., Morris, J.: Safety of stereo driver assistance systems. In: Proc. IEEE Symp. Intell. Vehicles (IV), pp. 469–475 (2012)
Khan, W., Klette, R.: Stereo accuracy for collision avoidance for varying collision trajectories. In: Proc. IEEE Symp. Intell. Vehicles (IV) (2013)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. IEEE Int. Conf. Computer Vision, pp. 1150–1157 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khan, W., Klette, R. (2014). Accuracy of Trajectories Estimation in a Driver-Assistance Context. In: Huang, F., Sugimoto, A. (eds) Image and Video Technology – PSIVT 2013 Workshops. PSIVT 2013. Lecture Notes in Computer Science, vol 8334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53926-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-53926-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53925-1
Online ISBN: 978-3-642-53926-8
eBook Packages: Computer ScienceComputer Science (R0)