Abstract
The goal of this work is to propose a solution to improve a driver’s safety while changing lanes on the highway. In fact, if the driver is not aware of the presence of a vehicle in his blindspot a crash can occur. In this article we propose a method to monitor the blindspot zone using video feeds and warn the driver of any dangerous situation. In order to fit in a real time embedded car safety system, we avoid using any complex techniques such as classification and learning. The blindspot monitoring algorithm we expose here is based on a features tracking approach by optical flow calculation. The features to track are chosen essentially given their motion patterns that must match those of a moving vehicle and are filtered in order to overcome the presence of noise. We can then take a decision on a car presence in the blindspot given the tracked features density. To illustrate our approach we present some results using video feeds captured on the highway.
Chapter PDF
Similar content being viewed by others
Keywords
- Motion Vector
- Stereo Vision
- Intelligent Transportation System
- Vehicle Detection
- Collision Avoidance System
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
Alvarez, S., Sotelo, M.A., Ocana, M., Llorca, D.F., Parra, I.: Vision-based target detection in road environments. In: WSEAS VIS 2008 (2008)
Balcones, D., Llorca, D., Sotelo, M., Gavilin, M., lvarez, S., Parra, I., Ocaa, M.: Real-time vision-based vehicle detection for rear-end collision mitigation systems. In: vol. 5717, pp. 320–325 (2009)
Batavia, P., Pomerleau, D., Thorpe, C.: Overtaking vehicle detection using implicit optical flow. In: IEEE Conference on Intelligent Transportation System, ITSC 1997, pp. 729–734 (November 1997)
Bertozzi, M., Broggi, A., Fascioli, A., Nichele, S.: Stereo vision-based vehicle detection. In: Proceedings of the IEEE on Intelligent Vehicles Symposium IV 2000, pp. 39–44 (2000)
Betke, M., Haritaoglu, E., Davis, L.S.: Real-time multiple vehicle detection and tracking from a moving vehicle. Machine Vision and Applications 12, 69–83 (2000), http://dx.doi.org/10.1007/s001380050126 , doi:10.1007/s001380050126
Chang, W.C., Hsu, K.J.: Vision-based side vehicle detection from a moving vehicle. In: International Conference on System Science and Engineering (ICSSE), 2010, pp. 553–558 (2010)
Chen, C., Chen, Y.: Real-time approaching vehicle detection in blind-spot area. In: 12th International IEEE Conference on Intelligent Transportation Systems, ITSC 2009, pp. 1–6 (2009)
Chern, M.Y.: Development of a vehicle vision system for vehicle/lane detection on highway. In: 18th IPPR Conf. on Computer Vision, Graphics and Image Processing, pp. 803–810 (2005)
Collado, J., Hilario, C., de la Escalera, A., Armingol, J.: Model based vehicle detection for intelligent vehicles. In: 2004 IEEE Intelligent Vehicles Symposium, pp. 572–577 (2004)
Cui, J., Liu, F., Li, Z., Jia, Z.: Vehicle localisation using a single camera. In: 2010 IEEE Intelligent Vehicles Symposium (IV), pp. 871–876 (2010)
Diaz Alonso, J., Ros Vidal, E., Rotter, A., Muhlenberg, M.: Lane-change decision aid system based on motion-driven vehicle tracking. IEEE Transactions on Vehicular Technology 57(5), 2736–2746 (2008)
Franke, U., Joos, A.: Real-time stereo vision for urban traffic scene understanding. In: Proceedings of the IEEE on Intelligent Vehicles Symposium IV 2000, pp. 273–278 (2000)
Jeong, S., Ban, S.W., Lee, M.: Autonomous detector using saliency map model and modified mean-shift tracking for a blind spot monitor in a car. In: Seventh International Conference on Machine Learning and Applications, ICMLA 2008, pp. 253–258 (2008)
Kato, T., Ninomiya, Y., Masaki, I.: An obstacle detection method by fusion of radar and motion stereo. IEEE Transactions on Intelligent Transportation Systems 3(3), 182–188 (2002)
Kim, S., Oh, S.Y., Kang, J., Ryu, Y., Kim, K., Park, S.C., Park, K.: Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), pp. 2173–2178 (2005)
Labayrade, R., Royere, C., Gruyer, D., Aubert, D.: Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner. Autonomous Robots 19, 117–140 (2005), http://dx.doi.org/10.1007/s10514-005-0611-7 , doi:10.1007/s10514-005-0611-7
Llorca, D.F., Snchez, S., Ocaa, M., Sotelo, M.A.: Vision-based traffic data collection sensor for automotive applications. Sensors 10(1), 860–875 (2010), http://www.mdpi.com/1424-8220/10/1/860/
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision, pp. 674–679 (1981)
Mar, J., Lin, H.T.: The car-following and lane-changing collision prevention system based on the cascaded fuzzy inference system. IEEE Transactions on Vehicular Technology 54(3), 910–924 (2005)
Negri, P., Clady, X., Prevost, L.: Benchmarking haar and histograms of oriented gradients features applied to vehicle detection. ICINCO-RA (1), 359–364 (2007)
She, K., Bebis, G., Gu, H., Miller, R.: Vehicle tracking using on-line fusion of color and shape features. In: Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems, pp. 731–736 (2004)
Shi, J., Tomasi, C.: Good features to track. In: 1994 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1994), pp. 593–600 (1994)
Song, K.T., Chen, C.H., Huang, C.H.C.: Design and experimental study of an ultrasonic sensor system for lateral collision avoidance at low speeds. In: 2004 IEEE Intelligent Vehicles Symposium, pp. 647–652 (2004)
Techmer, A.: Real time motion analysis for monitoring the rear and lateral road. In: 2004 IEEE Intelligent Vehicles Symposium, pp. 704–709 (2004)
Tsai, L.W., Hsieh, J.W., Fan, K.C.: Vehicle detection using normalized color and edge map. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 2, pp. II- 598–II-601 (2005)
Wang, Y.K., Chen, S.H.: A robust vehicle detection approach. In: IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2005, pp. 117–122 (2005)
Wender, S., Dietmayer, K.: 3d vehicle detection using a laser scanner and a video camera. Intelligent Transport Systems, IET 2(2), 105–112 (2008)
Wu, B.F., Chen, C.J., Li, Y.F., Yang, C.Y., Chien, H.C., Chang, C.W.: An embedded all-time blind spot warning system. In: Zeng, Z., Wang, J. (eds.) Advances in Neural Network Research and Applications. LNCS, vol. 67, pp. 679–685. Springer, Heidelberg (2010)
Wu, B.F., Chen, W.H., Chang, C.W., Chen, C.J., Chung, M.W.: A new vehicle detection with distance estimation for lane change warning systems. In: 2007 IEEE Intelligent Vehicles Symposium, pp. 698–703 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saboune, J., Arezoomand, M., Martel, L., Laganiere, R. (2011). A Visual Blindspot Monitoring System for Safe Lane Changes. In: Maino, G., Foresti, G.L. (eds) Image Analysis and Processing – ICIAP 2011. ICIAP 2011. Lecture Notes in Computer Science, vol 6979. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24088-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-24088-1_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24087-4
Online ISBN: 978-3-642-24088-1
eBook Packages: Computer ScienceComputer Science (R0)