A Real Time Method to Detect Vehicle for Collision Avoidance Applications

Abstract:

In this paper, we propose a real time method to detect vehicles on road by using a vehicle mounted monocular camera. Based on lane detection design, low time cost and high accuracy vehicle detecting and tracking algorithm is achieved. Robust and fast lane detection has been achieved using Hough transform in combination with a line merging method. The lane data are used for effective vehicle hypothesis generation. A subsequent validation, based on the area ratio of hypothesis vehicle, is used to eliminate false positives. Further, a data fusion mechanism is proposed to incorporate temporal information for stably updating vehicle detection results over time. Experimental results show that, without specific hardware and software optimizations, our method is able to detect vehicles on road with low false alarm rate at real time speeds of 30 frames per second.

• Periodical: Applied Mechanics and Materials (Volumes 719-720)
• Pages: 1132-1139
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.719-720.1132
• Online since: January 2015
• Authors: Fei Fei Yang*, Hai Zhang, Xiang Hong Li
• Keywords: Collision Avoidance, Computer Vision, Lane Detection, Vehicle Detection

References

[1] McCall, Joel C., and Mohan M. Trivedi. An integrated, robust approach to lane marking detection and lane tracking., Intelligent Vehicles Symposium, 2004 IEEE. IEEE, 2004, pp.533-537.

DOI: 10.1109/ivs.2004.1336440

[2] Sun, Zehang, George Bebis, and Ronald Miller. On-road vehicle detection: A review., Pattern Analysis and Machine Intelligence, IEEE Transactions on28. 5 (2006), pp.694-711.

DOI: 10.1109/tpami.2006.104

[3] Teoh, Soo Siang, and Thomas Bräunl. Symmetry-based monocular vehicle detection system., Machine Vision and Applications 23. 5 (2012), pp.831-842.

DOI: 10.1007/s00138-011-0355-7

[4] Matthews, N. D., et al. Vehicle detection and recognition in greyscale imagery., Control Engineering Practice 4. 4 (1996), pp.473-479.

DOI: 10.1016/0967-0661(96)00028-7

[5] Handmann, Uwe, et al. An image processing system for driver assistance., Image and Vision Computing 18. 5 (2000) , pp.367-376.

DOI: 10.1016/s0262-8856(99)00032-3

[6] Bertozzi, Massimo, et al. Stereo vision-based vehicle detection., IEEE Intelligent Vehicles Symposium. 2000, pp.39-44.

[7] Wang, Junxian, George Bebis, and Ronald Miller. Overtaking vehicle detection using dynamic and quasi-static background modeling., Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on. IEEE, 2005, pp.64-64.

DOI: 10.1109/cvpr.2005.506

[8] Tomasi, Carlo, and Takeo Kanade. Detection and tracking of point features. School of Computer Science, Carnegie Mellon Univ., (1991).

[9] Kiryati, Nahum, Yuval Eldar, and Alfred M. Bruckstein. A probabilistic Hough transform., Pattern recognition 24. 4 (1991), pp.303-316.

DOI: 10.1016/0031-3203(91)90073-e

[10] Kalal, Zdenek, Krystian Mikolajczyk, and Jiri Matas. Tracking-learning-detection., Pattern Analysis and Machine Intelligence, IEEE Transactions on34. 7 (2012), pp.1409-1422.

DOI: 10.1109/tpami.2011.239

[11] Betke, Margrit, Esin Haritaoglu, and Larry S. Davis. Real-time multiple vehicle detection and tracking from a moving vehicle., Machine vision and applications 12. 2 (2000) , pp.69-83.

DOI: 10.1007/s001380050126

[12] Sivaraman, Sayanan, and Mohan M. Trivedi. A general active-learning framework for on-road vehicle recognition and tracking., Intelligent Transportation Systems, IEEE Transactions on 11. 2 (2010) , pp.267-276.

DOI: 10.1109/tits.2010.2040177