Abstract
This article discusses a pedestrian detector for an experimental vehicle, based on visual information from a single far infrared camera. The system considers three consecutive processes for each image. Once the candidates’ heads have been extracted, regions of interest are resized based on the distance to the camera and then filtered by its vertical edges symmetry. Once the bounding boxes of possible pedestrians are picked a spatial correlation with some template models takes place. Finally, detected pedestrians are tracked, contrasting their position on successive frames. The results are satisfactory, classifying correctly almost 96% of pedestrians closer than 45m to the vehicle.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sun, Z., Bebis, G., Miller, R.: On-road vehicle detection: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5), 694–711 (2006)
Nanda, H., Davis, L.: Probabilistic Template Based Pedestrian Detection in Infrared Videos. In: Procs. IEEE Intelligent Vehicles Symposium 2002, Paris, France (June 2002)
Fang, Y., Yamada, K., Ninomiya, Y., Horn, B.K.P., Masaki, I.: A Shape-Independent-Method for Pedestrian Detection with Far-Infrared-Images. IEEE Transactions on Vehicular Technology 53(6) (November 2004)
Hilario, C., Collado, J.M., Armingol, J.M., de la Escalera, A.: Pedestrian Detection for Intelligent Vehicles based on Active Contour Models and Stereo Vision. In: 10th International Workshop on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain (February 2005)
Bu, F., Chan, C.-Y.: Pedestrian Detection in Transit Bus Application: Sensing Technologies and Safety Solutions. In: Intelligent Vehicles Symposium, June 13-15. IEEE, Los Alamitos (2007)
Broggi, A., Bertozzi, M., Chapuis, R., Fascioli, F.C.A., Tibaldi, A.: Pedestrian Localization and Tracking System with Kalman Filtering. In: Procs. IEEE Intelligent Vehicles Symposium 2004, Parma, Italy, June 2004, pp. 584–589 (2004)
Bertozzi, M., Hilario Gómez, C., Fedriga, R.I., Vezzoni, G., Del Rose, M.: Pedestrian Detection in Far Infrared Images based on the use of Probabilistic Templates. In: Intelligent Vehicles Symposium, June 13-15. IEEE, Los Alamitos (2007)
Binelli, E., Broggi, A., Fascioli, A., Ghidoni, S., Grisleri, P.: A modular tracking system for far infrared pedestrian recognition. In: Intelligent Vehicles Symposium, June 13-15. IEEE, Los Alamitos (2007)
Xu, F., Fujimura, K.: Pedestrian Detection and Tracking with Night Vision. In: Procs. IEEE Intelligent Vehicles Symposium 2002, Paris, France (June 2002)
Kittler, J., Illingoworth, J., Foglein, J.: Threshold Selection Based on a Simple Image Statistic. Computer Vision, Graphics, and Image Processing 30, 125–147 (1985)
Otsu, N.: A Threshold Selection Method from Gray-level Histograms. IEEE Transaction on Systems, Man and Cybernetics SMC-9,, 62–66 (1979)
Chang, C.-I., Du, Y., Wang, J., Guo, S.-M., Thouin, P.D.: Survey and comparative analysis of entropy and relative entropy thresholding techniques. IEE Proceedings Vision, Image and Signal Processing 153(6), 837–850 (2006)
Fuentes, L.M., Velastin, S.A.: People tracking in surveillance applications. In: Proceedings 2nd IEEE Int. Workshop on PETS, Kauai, Hawaii, USA, December 9 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Olmeda, D., Hilario, C., de la Escalera, A., Armingol, J.M. (2008). Pedestrian Detection and Tracking Based on Far Infrared Visual Information. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_87
Download citation
DOI: https://doi.org/10.1007/978-3-540-88458-3_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88457-6
Online ISBN: 978-3-540-88458-3
eBook Packages: Computer ScienceComputer Science (R0)