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Far infrared pedestrian detection and tracking for night driving

  • Daniel Olmeda (a1), Arturo de la Escalera (a1) and José María Armingol (a1)

Summary

This paper presents a module for pedestrian detection from a moving vehicle in low-light conditions. The algorithm make use of a single far infrared camera based on a microbolometer. Images of the area ahead of the vehicle are analyzed to determine if any pedestrian might be in its trajectory. Detection is achieved by searching for distributions of temperatures in the scene similar to that of the human body. Those areas with an appropriate temperature, size, and position in the image are classified, by means of a correlation between them and some probabilistic models, which represents the average temperature of the different parts of the human body. Finally, those pedestrians found are tracked in a subsequent step, using an unscented Kalman filter. This final stage of the algorithm enables the algorithm to predict the trajectory of the pedestrian, in a way that does not depend on the movement of the camera. The aim of this system is to warn the vehicle's driver and reduce the reaction time in case an emergency break is necessary.

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Corresponding author

*Corresponding author. E-mail: dolmeda@ing.uc3m.es

References

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1.Bertozzi, M., Broggi, A., Grisleri, P., Graf, T. and Meinecke, M., “Pedestrian Detection in Infrared Images,” Intelligent Vehicles Symposium, 2003. Proceedings. IEEE (May 2003) pp. 662–667.
2.Bertozzi, M., Broggi, A., Hilario, C., Fedriga, R., Vezzoni, G. and Del Rose, M., “Pedestrian Detection in Far Infrared Images Based on the Use of Probabilistic Templates. Intelligent Vehicles Symposium, 2007 IEEE (May 2007) pp. 327–332.
3.Bertozzi, M., Broggi, A., Del Rose, M., Felisa, M., Rakotomamonjy, A. and Suard, F., “A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier,” IEEE Intelligent Transportation Systems Conference (2007).
4.Binelli, E., Broggi, A., Fascioli, A., Ghidoni, S., Grisleri, P., Graf, T. and Meinecke, M.-M., “A modular tracking system for far infrared pedestrian recognition,” Proceedings of IEEE Intelligent Vehicles Symposium (2005) pp. 758–763.
5.Julier, S. and Uhlmann, J., “A new extension of the Kalman filter to nonlinear systems,” Int. Symp. Aerosp./Def. Sens. 3, 2638 (Jan. 1997).
6.Ling, B., Zeifman, M. I. and Gibson, D. R. P., “Multiple pedestrian detection using IR led stereo camera,” Intell. Robot Comput. Vision XXV: Algorithms, Tech. Active Vision 6764 (1), 67640A (2007).
7.Meuter, M., Iurgel, U., Park, S.-B. and Kummert, A., “The unscented Kalman filter for pedestrian tracking from a moving host,” Intelligent Vehicles Symposium, 2008 IEEE (2008) pp. 37–42.
8.Miezianko, R. and Pokrajac, D., “People Detection in Low Resolution Infrared Videos,” Computer Vision and Pattern Recognition Workshops, 2008. CVPR Workshops 2008. IEEE Computer Society Conference on, (May 2008) pp. 1–6.
9.Nanda, H. and Davis, L., “Probabilistic template based pedestrian detection in infrared videos,” Intell. Vehicle Symp., 2002. IEEE 1, 1520 (May 2002).
10.Olmeda, D., Hilario, C., Escalera, A. and Armingol, J. M., “Pedestrian Detection and Tracking Based on Far Infrared Visual Information,” Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems (2008) pp. 958–969.
11.Sun, Z., Bebis, G. and Miller, R., “On-road vehicle detection: A review,” Pattern Anal. Mach. Intell., IEEE Trans. 28 (5), 694711 (2006).
12.Wan, M. and Herve, J., “Adaptive target detection and matching for a pedestrian tracking system,” Syst. Man Cybern. 2006. SMC '06. IEEE Int. Conf. 6, 51735178 (Sep. 2006).
13.Xu, F., Liu, X. and Fujimura, K., “Pedestrian detection and tracking with night vision,” IEEE Trans. Intell. Transp. Syst. 6 (1), 6371 (Jan. 2005).
14.Zhang, L., Wu, B. and Nevatia, R., “Pedestrian detection in infrared images based on local shape features,” Comput. Vision Pattern Recognit., 2007. CVPR '07. IEEE Conf. (May 2007) pp. 1–8.

Keywords

Far infrared pedestrian detection and tracking for night driving

  • Daniel Olmeda (a1), Arturo de la Escalera (a1) and José María Armingol (a1)

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