Abstract
This chapter presents a tetravision (4-camera) system for the detection of pedestrians by means of the simultaneous use of two far infrared and visible camera stereo pairs. The main idea is to exploit the advantages of both far infrared and visible cameras to develop a system that combines the advantages of using far infrared or daylight technologies. Different approaches are used to process the two stereo flows in an independent fashion to produce a list of areas of attention that potentially contain pedestrians. Then, four different following approaches are used to refine and filter this list and to validate the presence of a pedestrian. Preliminary results show that the combined use of two vision systems as well as the use of different and independent validation steps enable the system to effectively detect pedestrians in different conditions of illumination and background.
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.
Chapter's References
NIT Phase II: Evaluation of Non-Intrusive Technologies for Traffic Detection. Technical Report SRF No. 3683, MN DOT Research Report, 2002
M. Bertozzi, A. Broggi, M. Del Rose, and A. Lasagni. Infrared Stereo Vision-Based Human Shape Detection. In Proceedings of IEEE Intelligent Vehicles Symposium 2005, pages 23–28, Las Vegas, June 2005
M. Bertozzi, A. Broggi, M. Felisa, G. Vezzoni, and M. Del Rose. Low-Level Pedestrian Detection by Means of Visible and Far Infra-red Tetra-vision. In Proceedings of IEEE Intelligent Vehicles Symposium 2006, pages 231–236, Tokyo, Japan, June 2006
D. Beymer and K. Konolige. Real-time Tracking of Multiple People Using Continuous Detection. In Proceedings of the International Conference on Computer Vision, Kerkyra, 1999
B. Bhanu and J. Han. Kinematics-Based Human Motion Analysis in Infrared Sequences. In Proceedings of IEEE International Workshop on Applications of Computer Vision, Orlando, 2002
A. Broggi, C. Caraffi, R. I. Fedriga, and P. Grisleri. Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation. In Proceedings of International IEEE Workshop on Machine Vision for Intelligent Vehicles, San Diego, June 2005
R. Cutler and L. S. Davis. Robust Real-time Periodic Motion Detection, Analysis and Applications. IEEE Transaction on Pattern Analysis and Machine Intelligence, 22(8):781–796, August 2000
M.-S. Dao, F.G.B.D. Natale, and A. Massa. Edge Potential Functions and Genetic Algorithms for Shape-Based Image Retrieval. In Proceedings of IEEE International Conference on Image Processing (ICIP'03), volume 2, pages 729–732, Barcelona, Spain, September 2003
M.-S. Dao, F.G.B.D. Natale, and A. Massa. Efficient Shape Matching Using Weighted Edge Potential Function. In Proceedings of 13th International Conference on Image Analysis and Processing (ICIAP'05), Cagliari, Italy, September 2005
J.W. Davis and V. Sharma. Robust Background-Subtraction for Person Detection in Thermal Imagery. In Proceedings of International IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum, Washington, D.C, 2004
M. Del Rose and P. Frederick. Pedestrian Detection. In Proceedings of Intelligent Vehicle Systems Symposium, Traverse City, MI, 2005
M. Del Rose, P. Frederick, and J. Reed. Pedestrian Detection for Anti-Tamper Vehicle Protection. In Proceedings of Ground Vehicle Survivability Symposium, Monterey, CA, 2005
A. Fod, A. Howard, and M.J. Mataric. Laser-Based People Tracking. In Proceedings of IEEE International Conference on Robotics and Automation, Washington, DC, 2002
K.C. Frerstenberg, J. Dietmayer, and V. Willhoeft. Pedestrian Recognition in Urban Traffic Using Vehicle Based Multilayer Laserscanner. In Proceedings of Automobile Engineers Cooperation International Conference, Paris, 2001
K.C. Frerstenberg and U. Lages. Pedestrian Detection and Classification by Laser-Scanners. In Proceedings of IEEE Intelligent Vehicles Symposium 2002, Paris, June 2002
K. Grauman and T. Darrell. Fast Contour Matching Using Approximate Earth Mover's Distance. Technical Report AI Memo, AIM-2003-026, MIT, Cambridege, MA, 2003
G.D. Hines, Z. Rahman, D.J. Jobson, and G.A. Woodell. Multi-image registration for an enhanced vision system, Proceedings - SPIE The International Society for Optical Engineering, pp. 231–241, April 2003
R. Kania, M. Del Rose, and P. Frederick. Autonomous Robotic Following Using Vision Based Techniques. In Proceedings of Ground Vehicle Survivability Symposium, Monterey, CA, 2005
M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active Contour Models. International Journal of Computer Vision, 1(4):321–331, 1988
M. Koltz and H. Rohling. 24 GHz Radar Sensors for Automotive Applications. In Proceedings of International Conference on Microwaves and Radar, Warsaw, Poland, 2000
R. Labayrade and D. Aubert. A Single Framework for Vehicle Roll, Pitch, Yaw Estimation and Obstacles Detection by Stereovision. In Proceedings of IEEE Intelligent Vehicles Symposium 2003, pages 31–36, Columbus, OH, June 2003
L. Lee, G. Dalley, and K. Tieu. Learning Pedestrian Models for Silhouette Refinement. In Proceedings IEEE International Conference on Computer Vision, Nice, France, 2003
S. Milch and M. Behrens. Pedestrian Detection with Radar and Computer Vision. In Proceedings of Conference on Progress in Automobile Lighting, Darmstadt, Germany, 2001
H. Nanda and L. Davis. Probabilistic Template Based Pedestrian Detection in Infrared Videos. In Proceedings of IEEE Intelligent Vehicles Symposium 2002, Paris, June 2002
C. Papageorgiou, T. Evgeniou, and T. Poggio. A Trainable Pedestrian Detection System. 38: 15–33, June 2000
A. Senior. Tracking People with Probabilistic Appearance Models. In ECCV Workshop on Performance Evaluation of Tracking and Surveillance Systems, 2002
A. Shashua, Y. Gdalyahu, and G. Hayun. Pedestrian Detection for Driving Assistance Systems: Single-frame Classification and System Level Performance. In Proceedings of IEEE Intelligent Vehicles Symposium 2004, Parma, Italy, June 2004
H. Shimizu and T. Poggie. Direction Estimation of Pedestrian from Multiple Still Images. In Proceedings of IEEE Intelligent Vehicles Symposium 2004, Parma, Italy, June 2004
G.P. Stein, O. Mano, and A. Shashua. Vision Based ACC with a Single Camera: Bounds on Range and Range Rate Accuracy. In Proceedings of IEEE Intelligent Vehicles Symposium 2003, Columbus, OH, June 2003
S. Tate and Y. Takefuji. Video-Based Human Shape Detection Deformable Templates and Neural Network. In Proceedings of Knowledge Engineering System Conference, Crema, Italy, 2002
D.J. Williams and M. Shah. A Fast Algorithm for Active Contours and Curvature Estimation. CVGIP: Image Understanding, 55(1):14–26, 1992
C. Xu and J. Prince. Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions on Image Processing, 7(3):359–369, 1998
F. Xu and K. Fujimura. Pedestrian Detection and Tracking with Night Vision. In Proceedings of IEEE Intelligent Vehicles Symposium 2002, Paris, June 2002
L. Zhao. Dressed Human Modeling, Detection, and Parts Localization. Ph.D. dissertation, Carnegie Mellon University, 2001
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Bertozzi, M. et al. (2009). Multi Stereo-Based Pedestrian Detection by Daylight and Far-Infrared Cameras. In: Hammoud, R.I. (eds) Augmented Vision Perception in Infrared. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84800-277-7_16
Download citation
DOI: https://doi.org/10.1007/978-1-84800-277-7_16
Publisher Name: Springer, London
Print ISBN: 978-1-84800-276-0
Online ISBN: 978-1-84800-277-7
eBook Packages: Computer ScienceComputer Science (R0)