Monocular Rear Approach Indicator for Motorcycles

Joerg Deigmoeller, Herbert Janssen, Oliver Fuchs, Julian Eggert

Abstract

Conventional rear-view mirrors on motorcycles only allow a limited visibility as they are shaky and cover a small field of view. Especially at high speeds with strong headwind, it is difficult for the rider to turn his head to observe blind spots. To support the rider in observing the rear and blind-spots, a monocular system that indicates approaching vehicles is proposed in this paper. The vision based indication relies on sparse optical flow estimation. In a first step, a rough separation of background and approaching object pixel motion is done in an efficient and computationally cheap way. In a post-processing step, pixel motion information is further checked on geometric meaningful transformations and continuity over time. As a prototype, the system has been mounted on a Honda Pan-European motorcycle plus monitor in the dashboard that shows the rear-view image to the rider. If an approaching object is detected, the rider gets an indication on the monitor. The rearview on the monitor not only acts as HMI (Human Machine Interface) for the indication, but also significantly extends the visibility compared to mirrors. The algorithm has been extensively evaluated for relative speeds from 20 km/h to 100 km/h (speed differences between motorcycle and approaching vehicle), at normal, rainy and night conditions. Results show that the approach offers a sensing range from 20 m at low speed up to 60 m at night.

References

  1. Audi (2013). Audi side assist - innovative driver assistance system. http://www.gizmag.com/audi-digitalrear-view-mirror-production/23681/.
  2. Bosch (2012). Side view assist. http://www. bosch-automotivetechnology.com/en/de/homepage/ homepage 1.html.
  3. Bouguet, J.-Y. (2000). Pyramidal implementation of the lucas kanade feature tracker description of the algorithm. http://robots.stanford.edu/cs223b04/ algo tracking.pdf.
  4. Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM.
  5. Hella (2012). Driver assistance systems. http://www. hella.com/hella-com/502.html?rdeLocaleAttr=en.
  6. Klappstein, J., Stein, F., and Franke, U. (2006). Monocular motion detection using spatial constraints in a unified manner. IEEE Intelligent Vehicles Symposium.
  7. Ma, Y., Soatto, S., Kosecka, J., and Sastry, S. S. (2004). An Invitation to 3-D Vision. Springer-Verlag, New York, 2nd edition.
  8. Mazda (2011). Mazda's rear vehicle monitoring system to receive euro ncap advanced award. http:// www.mazda.com/publicity/release/2011/201108/ 110825a.html.
  9. Mueller, D., Meuter, M., and Park, S.-B. (2008). Motion segmentation using interest points. IEEE Intelligent Vehicles Symposium.
  10. Nissan (2012). Multi-sensing system with rear camera. http://www.nissan-global.com/EN/TECHNOLOGY/ OVERVIEW/rear camera.html.
  11. OpenCV (2013). Open source computer vision library. http://opencv.willowgarage.com/wiki/.
  12. Quick, D. (2012). Audi's digital rear-view mirror moves from racetrack to r8 e-tron production vehicle. http://www.gizmag.com/audi-digital-rear-viewmirror-production/23681/.
  13. Rabe, C., Franke, U., and Gehrig, S. (2007). Fast detection of moving objects in complex scenarios. IEEE Intelligent Vehicles Symposium.
  14. Scaramuzza, D. and Siegwart, R. (2008). Monocular omnidirectional visual odometry for outdoor ground vehicles. Computer Vision Systems, Springer Lecture Notes in Computer Science.
  15. Schlipsing, M., Salmen, J., Lattke, B., Schroeter, K. G., and Winner, H. (2012). Roll angle estimation for motorcycles: Comparing video and inertial sensor approaches. IEEE Intelligent Vehicles Symposium.
  16. Shewchuk, J. R. (1996). Triangle: Engineering a 2d quality mesh generator and delaunay triangulator. Applied Computational Geometry: Towards Geometric Engineering.
  17. Shewchuk, J. R. (2002). Delaunay refinement algorithms for triangular mesh generation. Computational Geometry: Theory and Applications, 22:1-3.
  18. Stein, G., Mano, O., and Shashua, A. (2003). Vision-based acc with a single camera: bounds on range and range rate accuracy. IEEE Intelligent Vehicles Symposium.
  19. Stierlin, S. and Dietmayer, K. (2012). Scale change and ttc filter for longitudinal vehicle control based on monocular video. IEEE Intelligent Transportation Systems.
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Paper Citation


in Harvard Style

Deigmoeller J., Janssen H., Fuchs O. and Eggert J. (2014). Monocular Rear Approach Indicator for Motorcycles . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 474-480. DOI: 10.5220/0004668304740480


in Bibtex Style

@conference{visapp14,
author={Joerg Deigmoeller and Herbert Janssen and Oliver Fuchs and Julian Eggert},
title={Monocular Rear Approach Indicator for Motorcycles},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={474-480},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004668304740480},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Monocular Rear Approach Indicator for Motorcycles
SN - 978-989-758-009-3
AU - Deigmoeller J.
AU - Janssen H.
AU - Fuchs O.
AU - Eggert J.
PY - 2014
SP - 474
EP - 480
DO - 10.5220/0004668304740480