Abstract
The proposed approach improves preprocessing of image data for the line following robot. The tracking algorithm uses Track–Before–Detect algorithm using Viterbi algorithm. Proposed technique uses deep learning for the estimation of the line and background area. The segmentation improves detection of weak line on the image disturbed by numerous additive patterns and Gaussian noise.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Astrand, B., Baerveldt, A.: A vision-based row-following system for agricultural field machinery. Mechatronics 15(2), 251–269 (2005)
Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House, Norwood (1999)
Deans, S.R.: The Radon Transform and Some of Its Applications. Wiley, New York (1983)
Golightly, I., Jones, D.: Visual control of an unmanned aerial vehicle for power line inspection. In: 12th International Conference on Advanced Robotics, ICAR 2005, pp. 288–295, July 2005
Haykin, S., Moher, M.: Communication Systems. Wiley, Chichester (2009)
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)
LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 253–256, May 2010
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). http://dx.doi.org/10.1038/nature14539
Matczak, G., Mazurek, P.: History dependent viterbi algorithm for navigation purposes of line following robot. Image Process. Commun. 20(4), 5–11 (2016)
Mazurek, P.: Hierarchical track-before-detect algorithm for tracking of amplitude modulated signals. In: Choraś, R.S. (ed.) Image Processing & Communications Challenges 3. AISC, vol. 102, pp. 511–518. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23154-4_56
Mazurek, P.: Line estimation using the viterbi algorithm and track-before-detect approach for line following mobile robots. In: 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 788–793, September 2014
Mazurek, P., Putynkowski, G.: Frequency management for electromagnetic continuous wave conductivity meters. Sensors 16(4), 490 (2016)
Mazurek, P.: Directional filter and the Viterbi algorithm for line following robots. In: Chmielewski, L.J., Kozera, R., Shin, B.-S., Wojciechowski, K. (eds.) ICCVG 2014. LNCS, vol. 8671, pp. 428–435. Springer, Cham (2014). doi:10.1007/978-3-319-11331-9_51
Ollis, M.: Perception Algorithms for a Harvesting Robot. CMU-RI-TR-97-43, Carnegie Mellon University (1997)
Scott, T.A., Nilanjan, R.: Biomedical Image Analysis: Tracking. Morgan & Claypool, San Rafael (2005)
Stone, L., Barlow, C., Corwin, T.: Bayesian Multiple Target Tracking. Artech House, Norwood (1999)
Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 13(2), 260–269 (1967)
Wang, R.: Edge detection using convolutional neural network. In: Cheng, L., Liu, Q., Ronzhin, A. (eds.) ISNN 2016. LNCS, vol. 9719, pp. 12–20. Springer, Cham (2016). doi:10.1007/978-3-319-40663-3_2
Acknowledgments
This work is supported by the UE EFRR ZPORR project Z/2.32/I/1.3.1/267/05 “Szczecin University of Technology – Research and Education Center of Modern Multimedia Technologies” (Poland).
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Matczak, G., Mazurek, P. (2017). Dim Line Tracking Using Deep Learning for Autonomous Line Following Robot. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-57261-1_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57260-4
Online ISBN: 978-3-319-57261-1
eBook Packages: EngineeringEngineering (R0)