Scale Invariant Feature Transformed Based Vehicle Detection: A Review

International Journal of Computer & Organization Trends  (IJCOT)          
© 2015 by IJCOT Journal
Volume - 5 Issue - 5
Year of Publication : 2015
Authors :  Sheetal Madhukar Parate, Prof. Kemal Koche
DOI : 10.14445/22492593/IJCOT-V24P301


Sheetal Madhukar Parate, Prof. Kemal Koche "Scale Invariant Feature Transformed Based Vehicle Detection: A Review", International Journal of Computer & organization Trends (IJCOT), V5(5):1-9 Sep - Oct 2015, ISSN:2249-2593, Published by Seventh Sense Research Group.

Abstract Advanced driver assistance systems (ADAS) face many challenges in night-driving situations where due to poor illumination conditions the detection of other vehicles on the road becomes quite difficult. Traditional approaches attempt to use complex enhancement algorithms that consume a lot of computational power and are sensor dependent. This dissertation investigates the techniques for Scale-Invariant Feature Transform-based vehicle detection. A novel system that can heartily distinguish and track the development of vehicles in the video frames is proposed. The system consists of two noteworthy modules: a symmetry based item detector and a Kalman filter based vehicle tracker.


[1] Xiyan Chen, Qinggang Meng. Detecting Symmetry and Symmetric Constellations of Features. 2013 IEEE International Conference on Systems, Man, and Cybernetics.
[2] Gareth Loy, Jan-Olof Eklundh. Detecting Symmetry and Symmetric Constellations. Computer Vision–ECCV 2006, 2006 – Springe, Volume 3952, 2006, pp 508-521.
[3] Youpan Hu, Qing He, Xiaobin Zhuang, Haibin Wang, Baopu Li, Zhenfu Wen ,Bin Leng, Guan Guan, Dongjie Chen. Algorithm for vision-based vehicle detection and classification. Proceeding of the IEEE International Conference on Robotics and Biomimetics (ROBIO) Shenzhen, China, December 2013.
[4] Olaf Gietelink. Design and Validation of Advanced Driver Assistance Systems. PhD thesis, Technical University of Delft, 2007.
[5] aunl. Symmetry-based monocular vehicle detection system. Machine Vision and Applications, 2011, (DOI) 10.1007/s00138-011-0355-7.
[6] Sun Zehang, G. Bebis, and R. Miller. On-road vehicle detection: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5):694–711, 2006.
[7] B. Steux, C. Laurgeau, L. Salesse, and D. Wautier. Fade: a vehicle detection and tracking system featuring monocular color vision and radar data fusion. IEEE Intelligent Vehicle Symposium, 2002, volume 2, pages 632–639 vol.2, 2002.
[8] N. Srinivasa, Chen Yang, and C. Daniell. A fusion system for real-time forward collision warning in automobiles. Proceedings of the IEEE Intelligent Transportation Systems, volume 1, pages 457–462 vol.1, 2003.
[9] U. Kadow, G. Schneider, and A. Vukotich. Radar-Vision Based Vehicle Recognition with Evolutionary Optimized and Boosted Features. IEEE Intelligent Vehicles Symposium, 2007, pages 749–754, 2007.
[10] Sergiu Nedevschi, Andrei Vatavu, Florin Oniga, and Marc Michael Meinecke. Forward collision detection using a Stereo Vision System. 4th International Conference on Intelligent Computer Communication and Processing, 2008. (ICCP 2008), pages 115–122, 2008.
[11] Kunsoo Huh, Jaehak Park, Junyeon Hwang, and Daegun Hong. A stereo vision-based obstacle detection system in vehicles. Optics and Lasers in Engineering, 46(2):168– 178, 2008.
[12] M. Bertozzi, A. Broggi, A. Fascioli, and S. Nichele. Stereo vision-based vehicle detection. Proceedings of the IEEE Intelligent Vehicles Symposium, 2000. (IV 2000), pages 39–44, 2000.
[13] B. K. P. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17: 185–203, 1981.
[14] S. M. Smith and J. M. Brady. ASSET-2: real-time motion segmentation and shape tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(8):814– 820, 1995.
[15] B. Heisele and W. Ritter. Obstacle detection based on color blob flow. Proceedings of the Intelligent Vehicles ’95 Symposium, pages 282–286, 1995.
[16] S. M. Smith and J. M. Brady. SUSAN - a new approach to low level image processing. International Journal of Computer Vision, 1(23):45–78, 1997.
[17] C. Harris and M. Stephens. A Combined Corner and Edge Detection. Proceedings of The Fourth Alvey Vision Conference, pages 147–151, 1988.
[18] Cao Yanpeng, Alasdair Renfrew, and Peter Cook. Vehicle motion analysis based on a monocular vision system. Road Transport Information and Control - RTIC 2008 and ITS United Kingdom Members’ Conference, IET, pages 1–6, 2008.
[19] Cao Yanpeng, A. Renfrew, and P. Cook. Novel optical flow optimization using pulsecoupled neural network and smallest univalue segment assimilating nucleus. International Symposium on Intelligent Signal Processing and Communication Systems, (ISPACS 2007), pages 264–267, 2007.
[20] D.Willersinn and W. Enkelmann. Robust obstacle detection and tracking by motion analysis. IEEE Conference on Intelligent Transportation System, 1997. (ITSC ’97), pages 717–722, 1997.
[21] Christos Tzomakas and Werner von Seelen. Vehicle detection in traffic scenes using shadows. Technical report, Institut fur Neuroinformatik, Ruhr-Universitat, Bochum, Germany, 1998.
[22] M. B. Van Leeuwen and F. C. A. Groen. Vehicle detection with a mobile camera: spotting midrange, distant, and passing cars. Robotics & Automation Magazine, IEEE, 12(1): 37–43, 2005.
[23] Margrit Betke, Esin Haritaoglu, and Larry S. Davis. Realtime multiple vehicle detection and tracking from a moving vehicle. Machine Vision and Applications, 12(2):69–83, 2000.
[24] M. Bertozzi, S. Broggi and A. Castelluccio. A real-time oriented system for vehicle detection. Journal of Systems Architecture, 43:317–325, 1997.
[25] T. Zielke, M. Brauckmann, and W. V. Seelen. Intensity and edge-based symmetry detection with an application to car-following. CVGIP: Image Understanding, 58(2), pages 177–190, 1993.
[26] A. Bensrhair, A. Bertozzi, A. Broggi, A. Fascioli, S. Mousset, and G. Toulminet. Stereo vision-based feature extraction for vehicle detection. IEEE Intelligent Vehicle Symposium, 2002, volume 2, pages 465–470 vol.2, 2002.

Vehicle Detection, Scale invariant feature transform (SIFT), keypont.