Abstract
Line following robots are used in numerous application areas. The tracking of weak line is challenging, especially if SNR is high, so application of Track–Before–Detect algorithm is necessary. The Viterbi algorithm is assumed in this paper and the possibilities of optimization are considered. Two metric are applied in Monte Carlo tests—the direct metric and proposed boundary metric. The optimization of Viterbi algorithm is based on non single row movements of moving window.
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Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House (1999)
Chen, Z., Ellis, T.: Automatic lane detection from vehicle motion trajectories. In: Workshop on Vehicle Retrieval in Surveillance (VRS) in conjunction with 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 466–471 (2013)
Frejlichowski, D., Gościewska, K., Forczmański, P., Nowosielski, A., Hofman, R.: The removal of false detections from foreground regions extracted using adaptive background modelling for a visual surveillance system. Computer Information Systems and Industrial Management. Lecture Notes in Computer Science, vol. 8104, pp. 253–264. Springer, Berlin (2013)
Marchewka, A.: Crack detection on asphalt surface image using local minimum analysis. Adv. Intel. Soft Comput. 84, 353–359 (2010)
Mazurek, P.: Optimization of bayesian track-before-detect algorithms for GPGPUs implementations. Electr. Rev. R. 86(7), 187–189 (2010)
Mazurek, P.: Code reordering using local random extraction and insertion (LREI) operator for GPGPU-based track-before-detect systems. Soft Comput. 18(6), 1095–1106 (2013)
Mazurek, P.: Directional filter and the viterbi algorithm for line following robots, Computer Vision and Graphics, ICCVG 2014, LNCS, vol. 8671, pp. 428–435. Springer (2014)
Mazurek, P.: Line estimation using the viterbi algorithm and track-before-detect approach for line following mobile robots. pp. 788–793 (2014)
Mazurek, P.: Parallel distributed downsampled spatio-temporal track-before-detect algorithm. pp. 119–124 (2014)
Mazurek, P.: Viterbi algorithm for noise line following robots. Adv. Intel. Syst. Comput. AISC 313, 111–118 (2015)
Okarma, K., Frejlichowski, D., Czapiewski, P., Forczmań\({\check{{\rm D}}}\)ski, P., Hofman, R.: Similarity estimation of textile materials based on image quality assessment methods. Lect. Notes Comput. Sci. 8671, 478–485 (2014)
Ollis, M.: Perception Algorithms for a Harvesting Robot. CMU-RI-TR-97-43, Carnegie Mellon University (1997)
Stone, L., Barlow, C., Corwin, T.: Bayesian Multiple Target Tracking. Artech House (1999)
Taubel, G., Yang, J.S.: A lane departure warning system based on the integration of the optical flow and Hough transform methods. In: 2013 10th IEEE International Conference on Control and Automation (ICCA) Hangzhou, China, June 12–14, pp. 1352–1357 (2013)
Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 13(2), 260–269 (1967)
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Matczak, G., Mazurek, P. (2016). Adjustment of Viterbi Algorithm for Line Following Robots. In: Choraś, R. (eds) Image Processing and Communications Challenges 7. Advances in Intelligent Systems and Computing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-319-23814-2_19
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DOI: https://doi.org/10.1007/978-3-319-23814-2_19
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