DOI QR코드

DOI QR Code

New Vehicle Verification Scheme for Blind Spot Area Based on Imaging Sensor System

  • Received : 2017.03.29
  • Accepted : 2017.04.09
  • Published : 2017.03.31

Abstract

Ubiquitous computing is a novel paradigm that is rapidly gaining in the scenario of wireless communications and telecommunications for realizing smart world. As rapid development of sensor technology, smart sensor system becomes more popular in automobile or vehicle. In this study, a new vehicle detection mechanism in real-time for blind spot area is proposed based on imaging sensors. To determine the position of other vehicles on the road is important for operation of driver assistance systems (DASs) to increase driving safety. As the result, blind spot detection of vehicles is addressed using an automobile detection algorithm for blind spots. The proposed vehicle verification utilizes the height and angle of a rear-looking vehicle mounted camera. Candidate vehicle information is extracted using adaptive shadow detection based on brightness values of an image of a vehicle area. The vehicle is verified using a training set with Haar-like features of candidate vehicles. Using these processes, moving vehicles can be detected in blind spots. The detection ratio of true vehicles was 91.1% in blind spots based on various experimental results.

Keywords

References

  1. Z. Sun, G. Bebis, and R. Miller, "On-road vehicle detection: A review," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 5, pp. 694-711, May 2006. https://doi.org/10.1109/TPAMI.2006.104
  2. K. Goswami and B. G. Kim, "A Novel Mesh-Based Moving Object Detection Technique in Video Sequence," Journal of Convergence, Vol. 4, No.1, pp. 20-24, 2013.
  3. Shahabi, Cyrus, et al. "Janus-Multi Source Event Detection and Collection System for Effective Surveillance of Criminal Activity," Journal of information processing systems, Vol. 10, No. 1, pp. 1- 22, 2014. https://doi.org/10.3745/JIPS.2014.10.1.001
  4. M. Mohammad and O. Murad, "Artificial neuro fuzzy logic system for detecting human emotions," Humancentric Computing and Information Sciences, vol. 3, No. 1, pp. 1-13, 2013. https://doi.org/10.1186/2192-1962-3-1
  5. http://www.internationaltransportforum.org/Pub/pdf/ 13KeyStat2012.pdf.
  6. M. M. Trivedi and S. Cheng, "Holistic sensing and active displays for intelligent driver support systems," Computer, vol. 40, no. 5, pp. 60-68, May 2007. https://doi.org/10.1109/MC.2007.170
  7. M. Enzweiler and D. M. Gavrila, "A mixed generativediscriminative framework for pedestrian classification," in Proc. IEEE Conf. Comput.Vis. Pattern Recog., pp. 1-8, 2008.
  8. P. M. Roth and H. Bischof, "Active sampling via tracking," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1-8, June 2008.
  9. Kapoor, Ashish, et al. "Active learning with gaussian processes for object categorization," in Computer Vision, ICCV 2007 IEEE 11th International Conference on IEEE, pp.1-8, October 2007.
  10. S. Zehang, G. Bebis, and R. Miller, "Monocular precrash vehicle detection: features and classifiers," IEEE Transactions on Image Processing, Vol. 15, No. 7, pp. 2019-2034, 2006. https://doi.org/10.1109/TIP.2006.877062
  11. D. Balcones, et al., "Real-time vision-based vehicle detection for rear-end collision mitigation systems," In Computer Aided Systems Theory-EUROCAST 2009, Springer Berlin Heidelberg, pp.320-325, 2009.
  12. D. Alonso, L. Salgado, and M. Nieto, "Robust vehicle detection through multidimensional classification for on board video based systems," in IEEE International Conference on Image Processing, Vol. 4, No. 4, pp. 321-324, September 2007.
  13. G. Song, K. Y. Lee, and J. W. Lee, "Vehicle detection by edge-based candidate generation and appearancebased classification," in IEEE Intelligent Vehicles Symposium, pp. 428-433, June 2008.
  14. C. C. Lin, et al., "Development of a Multimedia- Based vehicle lane departure warning, forward collision warning and event video recorder systems," In Nine-th IEEE International Symposium on Multimedia Workshops, pp. 122-129 December 2007.
  15. D. Balcones, et al., "Real-time vision-based vehicle detection for rear-end collision mitigation systems," In Computer Aided Systems Theory-EUROCAST 2009 Springer Berlin Heidelberg, pp. 320-325, 2009.
  16. D. Alonso, L. Salgado, and M. Nieto, "Robust vehicle detection through multidimensional classification for on board video based systems," in IEEE International Conference on Image Processing, Vol. 4. No. 4 pp. 321-324, September 2007.
  17. G. Y. Song, K. Y. Lee, and J. W. Lee, "Vehicle detection by edge-based candidate generation and appearance-based classification," in IEEE Intelligent Vehicles Symposium, pp. 428-433, June 2008.
  18. Wu, Yao-Jan, et al. "Image processing techniques for lane-related information extraction and multi-vehicle detection in intelligent highway vehicles." Int. J. Automotive Technology 8.4: pp. 513-520, 2007.
  19. S. Vijayanarasimhan and K. Grauman, "Multi-level active prediction of useful image annotations for recognition," in Proc. Neural Inf. Process. Syst. Conf., pp. 1705-1712, 2008.
  20. R. E. Schapire and Y. Singer, "Improved boosting using confidence rated predictions," Machine Learning, vol. 37, no. 3, pp. 297-336, 1999. https://doi.org/10.1023/A:1007614523901
  21. P. Viola, and M. Jones, "Rapid object detection using a boosted cascade of simple features," in IEEE Proceedings of Computer Vision and Pattern Recognition, Vol. 1, No. 1, pp. 511-518, 2001.