THERMAL SCIENCE

International Scientific Journal

ADVANCED THERMAL CAMERA BASED SYSTEM FOR OBJECT DETECTION ON RAIL TRACKS

ABSTRACT
In this paper, an advanced thermal camera-based system for detection of objects on rail tracks is presented. Developed system is powered by advanced image processing algorithm, in order to achieve greater reliability and robustness, and tested on set of infrared images captured at night conditions. The goal of this system is to detect objects on rail tracks and next to them and estimate distances between camera stand and detected objects. For that purpose, different edge detection methods are tested, and finally Canny edge detector is selected for rail track detection and for determination of region of interest, further used for analysis in object detection process. In determined region of interest, region-based segmentation is used for object detection. For estimation of distances between camera stand and detected objects, homography based method is used. Validation of estimated distances is done, in respect to real measured distances from camera stand to objects (humans) involved in experiment. Distances are estimated with a maximum error of 2%. System can provide reliable object detection, as well as distance estimation, and for improved robustness and adaptability, artificial intelligence tools can be used.
KEYWORDS
PAPER SUBMITTED: 2018-05-04
PAPER REVISED: 2018-08-18
PAPER ACCEPTED: 2018-08-21
PUBLISHED ONLINE: 2019-01-19
DOI REFERENCE: https://doi.org/10.2298/TSCI18S5551P
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2018, VOLUME 22, ISSUE Supplement 5, PAGES [S1551 - S1561]
REFERENCES
  1. Pavlović, M., et al., Methods for Detection of Obstacles on the Railway Level Crossing, Proceedings, 17th Scientific-Expert Conference on Railways RAILCON ‘16, Nis, Serbia, 2016, pp. 121-124
  2. Pavlović, M., et al., Application of Thermal Imaging Systems for Object Detection, Proceedings, 13th International Conference on Accomplishments in Mechanical and Industrial Engineering, Banja Luka, Republic of Srpska, BiH, 2017, pp. 653-662
  3. Shaik, J., Iftekharuddin, K. M. Detection and Tracking of Targets in Infrared Images Using Bayesian Techniques, Optics & Laser Technology, 41 (2009), 6, pp. 832-842
  4. Iwasaki, Y., et al., Robust Vehicle Detection under Various Environmental Conditions Using an Infrared Thermal Camera and Its Application to Road Traffic Flow Monitoring, Sensors, 13 (2013), 6, pp. 7756-7773
  5. Jueungling, K., Arens, M., Feature Based Person Detection beyond the Visible Spectrum, Proceedings, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Miami, Fla., USA, 2009, pp. 30-37
  6. Wang, W., et al., Improved Human Detection and Classification in Thermal Images, Proceedings, 17th IEEE International Conference on Image Processing, Beijing, 2010, pp. 2313-2316
  7. Fernandez-Caballero, A., et al., Optical Flow or Image Subtraction in Human Detection from Infrared Camera on Mobile Robot, Robotics and Autonomous Systems, 58 (2010), 12, pp. 1273-1281
  8. Ćirić, I., et al., Computationally Intelligent System for Thermal Vision People Detection and Tracking in Robotic Applications, Proceedings, 11th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS), Nis, Serbia, 2013, pp. 587-590
  9. Bertozzi, M., et al., Pedestrian Detection by Means of Far-Infrared Stereo Vision, Computer Vision and Image Understanding, 106 (2007), 2-3, pp. 194-204
  10. Broggi, A., et al., A Multi-Resolution Approach for Infrared Vision-Based Pedestrian Detection, Pro-ceedings, IEEE Intelligent Vehicles Symposium, Parma, Italy, 2004, pp. 2313-2316
  11. Forth, A., Zamjatnins, F., Night-Vision Device for Railway Vehicles for Improving Safety, Sig-nal+Draht, 107 (2015), pp. 38-43
  12. Berg, A., et al., Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera, Proceedings, 19th Scandinavian Conference SCIA 2015, Copenhagen, Denmark, 2015, pp. 492-503
  13. Acharya, T., Ray, A. K., Image Processing: Principles and Applications, John Wiley & Sons Inc., Ho-boken, N. Y., USA, 2005.
  14. Nadernejad, E. , et al., Edge Detection Techniques: Evaluations and Comparisons, Applied Mathemati-cal Sciences, 31 (2008), 2, pp. 1507-1520
  15. Moeslund, T., Canny Edge Detection, Laboratory of Computer Vision and Media Technology, Aalborg University, Aalborg, Denmark, 2009
  16. Gonzales, R. C., Woods, R. E., Digital Image Processing, Pearson Education, Hoboken, N. Y., USA, 2008
  17. Ćirić, I., et al., Intelligent Optimal Control of Thermal Vision-Based Person-Following Robot Platform, Thermal Science, 18 (2014), 3, pp. 957-966
  18. Ćirić, I., et al., Thermal Vision Based Intelligent System for Human Detection and Tracking in Mobile Robot Control System, Thermal Science, 20 (2016), Suppl. 5, pp. S1553-S1559
  19. Hartley R., Zisserman A., Multiple View Geometry in Computer Vision, Cambridge University Press, New York, USA, 2004

© 2019 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence