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Vehicle detection and tracking for visual understanding of road environments

Published online by Cambridge University Press:  10 December 2009

Arturo de la Escalera  and
Jose Maria Armingol
Arturo de la Escalera*
Affiliation:
Intelligent Systems Laboratory, Department of Systems Engineering and Automation, Universidad Carlos III de Madrid, 28911, Leganes, Spain
Jose Maria Armingol
Affiliation:
Intelligent Systems Laboratory, Department of Systems Engineering and Automation, Universidad Carlos III de Madrid, 28911, Leganes, Spain
*
*Corresponding author. E-mail: [email protected]
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Summary

Many of the advanced driver assistance systems have the goal of perceiving the surroundings of a vehicle. One of them, adaptive cruise control, takes charge of searching for other vehicles in order to detect and track them with the aim of maintaining a safe distance and to avoid dangerous maneuvers. In the research described in this article, this task is accomplished using an on board camera. Depending on when the vehicles are detected the system analyzes movement or uses a vehicle geometrical model to perceive them. After, the detected vehicle is tracked and its behavior established. Optical flow is used for movement while the geometric model is associated with a likelihood function that includes information of the shape and symmetry of the vehicle and the shadow it casts. A genetic algorithm finds the optimum parameter values of this function for every image. As the algorithm receives information from a road detection module some geometric restrictions are applied. Additionally, a multiresolution approach is used to speed up the algorithm. Examples of real image sequences under different weather conditions are shown to validate the algorithm.

Keywords

Computer visionAdvanced driver assistance systemsObject detectionRoad vehicles
Type
Article
Information
Robotica , Volume 28 , Issue 6 , October 2010 , pp. 847 - 860
Copyright
Copyright © Cambridge University Press 2009

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References

1.Perrollaz, M., Labayrade, R., Royere, C., Hautiere, N. and Aubert, D., “Long Range Obstacle Detection Using Laser Scanner and Stereovision,” IEEE Intelligent Vehicles Symposium, Tokyo, Japan (2006) pp. 182–187.CrossRefGoogle Scholar
2.Bertozzi, M., Broggi, A., Cellario, M., Fasciolo, A., Lombardi, P. and Porta, M., “Artificial vision in road vehicles,” Proc. IEEE 90 (7), 1258–1271 (Jul. 2002).CrossRefGoogle Scholar
3.Charkari, N. and Mori, H., “Visual vehicle detection and tracking based on the sign pattern,” Adv. Robot. 9, 367–382 (1995).CrossRefGoogle Scholar
4.Kyo, S., Koga, T., Sakurai, K. and Okazaki, S., “A Robust Vehicle Detecting and Tracking System for Wet Weather Conditions Using the Imap-Vision Image Processing Board,” IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, Tokyo, Japan (1999) pp. 423–428.Google Scholar
5.Handmann, U., Kalinke, T., Tzomakas, C., Werner, M., Goerick, C. and von Seelen, W., “An image processing system for driver assistance,” Image Vis. Comput. 18, 367–376 (2000).CrossRefGoogle Scholar
6.Broggi, A., Bertozzi, M., Fascioli, A., Guarino, C. and Piazzi, A., “Visual perception of obstacles and vehicles for platooning,” IEEE Trans. Intel. Transp. Syst. 1 (3), 164–176 (2000).CrossRefGoogle Scholar
7.Sotelo, M., Fernandez, D., Naranjo, J., Gonzalez, C., Garcia, R., de Pedro, T. and Reviejo, J., “Vision-Based Adaptive Cruise Control for Intelligent Road Vehicles,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (2004) pp. 64–69.Google Scholar
8.Kaszubiak, J., Tornow, M., Kuhn, R., Michaelis, B. and Knoeppel, C., “Real-Time Vehicle and Lane Detection with Embedded Hardware,” IEEE Intelligent Vehicles Symposium, Las Vegas, NV, USA (2005) pp. 619– 624.Google Scholar
9.Toulminet, G., Bertozzi, M., Mousset, S., Bensrhair, A. and Broggi, A., “Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis,” IEEE Trans. Image Process. 15 (8), 2364–2375 (2006).CrossRefGoogle ScholarPubMed
10.Kazemi, F., Samadi, S., Poorreza, H. and Akbarzadeh, M., “Vehicle Recognition Using Curvelet Transform and SVM,” International Conference on Information Technology, Rourkela, India (2007) pp. 516–521.Google Scholar
11.Khammari, A., Nashashibi, F., Abramson, Y. and Laurgeau, C., “Vehicle Detection Combining Gradient Analysis and Adaboost Classification,” IEEE Intelligent Transportation Systems Conference, Vienna, Austria (2005) pp. 66–71.Google Scholar
12.Chan, Y.-M., Huang, S.-S., Fu, L.-C. and Hsiao, P.-Y., “Vehicle Detection Under Various Lighting Conditions by Incorporating Particle Filter,” IEEE Intelligent Transportation Systems Conference, Seattle, WA, USA (2007) pp. 534–539.Google Scholar
13.Dellaert, F. and Thorpe, C., “Robust car tracking using kalman filtering and bayesian templates,” SPIE Conf. Intel. Transp. Syst. 3207, 72–83 (1997).Google Scholar
14.Shali, H., Mertens, M. and Cornelis, J., “Model-Based Car Tracking Through the Integration of Search and Estimation,” SPIE Conference on Enhance and Synthetic Vision, Orlando, FL, USA (1998) pp. 160–166.Google Scholar
15.Schwarzinger, M., Zielke, T., Noll, D., Brauckmann, M. and von Seelen, W., “Vision-Based Car-Following: Detection, Tracking, and Identification,” IEEE Intelligent Vehicles Symposium, Detroit, MI, USA (1992) pp. 24–29.Google Scholar
16.Liu, T., Zheng, N., Zhao, L. and Cheng, H., “Learning Based Symmetric Features Selection for Vehicle Detection,” IEEE Intelligent Vehicles Symposium, Las Vegas, NV, USA (2005) pp. 124–129.Google Scholar
17.Ponsa, D., Lopez, A., Lumbreras, F., Serrat, J. and Graf, T., “3d Vehicle Sensor Based on Monocular Vision,” IEEE Conference on Intelligent Transportation Systems, Vienna, Austria (2005) pp. 1096–1101.Google Scholar
18.Alefs, B., “Embedded Vehicle Detection by Boosting,” IEEE Intelligent Transportation Systems Conference, Toronto, Canada (2006) pp. 536–541.Google Scholar
19.Rahman, M. and Santhanam, A., “Appearance-Based Classification of Moving Cars by Eigendimension Matching,” IEEE Intelligent Vehicles Symposium, Tokyo, Japan (2006) pp. 250–255.CrossRefGoogle Scholar
20.Sun, Z., Bebis, G. and Miller, R., “Improving the Performance of On-Road Vehicle Detection by Combining Gabor and Wavelet Features,” IEEE International Conference on Intelligent Transporation Systems, Singapore (2002) pp. 130–135.Google Scholar
21.Sun, Z., Bebis, G. and Miller, R., “Object detection using feature subset selection,” Pattern Recognit. 37, 2165–2176 (2004).CrossRefGoogle Scholar
22.Wang, C.-C. R. and Lien, J.-J. J., “Automatic vehicle detection using local features: A statistical approach,” IEEE Trans. Intell. Transp. Syst. 9, 83–96 (2008).CrossRefGoogle Scholar
23.Ludwig, O. and Nunes, U., “Improving the Generalization Properties of Neural Networks: An Application to Vehicle Detection,” Eleventh International IEEE Conference on Intelligent Transportation Systems, Beijing, China (2008) pp. 310–315.Google Scholar
24.Ponsa, D., Lopez, A., Serrat, J., Lumbreras, F. and Graf, T., “Multiple Vehicle 3d Tracking Using an Unscented Kalman,” IEEE Intelligent Transportation Systems Conference, Vienna, Austria (2005) pp. 1108–1113.Google Scholar
25.Chateau, T. and Lapreste, J., “Robust Real Time Tracking of a Vehicle by Image Processing,” IEEE Intelligent Vehicles Symposium, Parma, Italy (2004) pp. 315–318.Google Scholar
26.She, K., Bebis, G., Gu, H. and Miller, R., “Vehicle Tracking Using On-Line Fusion of Color and Shape Features,” IEEE International Conference on Intelligent Transportation Systems, Parma, Italy (2004) pp. 731–736.Google Scholar
27.Hilario, C., Collado, J. M., Armingol, J. M. and de la Escalera, A., “Visual Perception and Tracking of Vehicles for Driver Assistance Systems,” IEEE Intelligent Vehicles Symposium, Tokyo, Japan (2006) pp. 94–99.CrossRefGoogle Scholar
28.Batavia, P., Pomerleau, D. and Thorpe, C., “Overtaking Vehicle Detection Using Implicit Optical Flow,” IEEE Transportation Systems Conference, Boston, MA, USA (1997) pp. 729–734.Google Scholar
29.Baehring, D., Simon, S., Niehsen, W. and Stiller, C., “Detection of Close Cut-in and Overtaking Vehicles for Driver Assistance Based on Planar Parallax,” IEEE Intelligent Vehicles Symposium, Las Vegas, NV, USA (2005) pp. 290–295.Google Scholar
30.Dubuisson, M.-P., Lakshmanan, S. and Jain, A. K., “Vehicle Segmentation and Classification Using Deformable Templates,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 293–308 (1998).CrossRefGoogle Scholar
31.Collado, J., Hilario, C., de la Escalera, A. and Armingol, J., “Model Based Vehicle Detection for Intelligent Vehicles,” IEEE Intelligent Vehicles Symposium, Parma, Italy (2004) pp. 572–577.Google Scholar
32.Collado, J., Hilario, C., de la Escalera, A. and Armingol, J., “Self-Calibration of an On-Board Stereo-Vision System for Driver Assistance Systems,” IEEE Intelligent Vehicles Symposium, Tokyo, Japan (2006) pp. 156–162.CrossRefGoogle Scholar
33.Collado, J., Hilario, C., de la Escalera, A. and Armingol, J., “Adaptative road lanes detection and classification,” Lecture Notes Comput. Sci. 4179, 1151–1162 (2006).CrossRefGoogle Scholar
34.Sun, Z., Bebis, G. and Miller, R., “On-road vehicle detection: A review,” IEEE Trans. Pattern Anal. Mach. Intell. 28 (5), 694–711 (2006).Google ScholarPubMed
35.Goldberg, D., Genetic Algorithms in Search, Optimization and Machine Learning, Boston, MA, USA (Addison-Wesley, 1989).Google Scholar
36.Stein, G., Mano, O. and Shashua, A., “Vision-Based ACC with a Single Camera: Bounds on Range and Range Rate Accuracy,” IEEE Intelligent Vehicles Symposium, Columbus, O, USA (2003) pp. 120–125.Google Scholar