Abstract
Laser spot is widely used in numerous fields such as pointing during presentations, guiding robots in human machine interaction (HCI), and guiding munitions to targets in military, etc.. The interest of this paper is to develop an effective method for laser spot detection and tracking with exploiting fewer features. In this paper, a new technique jointly combined pyramid Lucas–Kanade (PLK) optical flow for detection and extended Kalman filter for accurately tracking laser spot in low-resolution and varying background video frames is presented. By using this new technique, only deploying intensity of laser spot with displacement of laser spot could get a very good result. This technique could be embedded into multi-core processing devices with inexpensive cost for potential high benefits in the future.
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References
Mikawa, M., Morimoto, Y., Tanaka, K.: Guidance method using laser pointer and gestures for librarian robot. In: 2010 IEEE RO-MAN. IEEE (2010)
Livingston, P.M.: Target-tracking laser designation. U.S. Patent No. 5,973,309, 26 October 1999
Chávez, F., et al.: Automatic laser pointer detection algorithm for environment control device systems based on template matching and genetic tuning of fuzzy rule-based systems. Int. J. Comput. Intell. Syst. 5(2), 368–386 (2012)
Meško, M., Toth, Š.: Laser spot detection. J. Inf. Control Manage. Syst. 11(1) (2013)
Sun, Y., Wang, B., Zhang, Y.: A method of detection and tracking for laser spot. In: Measuring Technology and Mechatronics Automation in Electrical Engineering, pp. 43–49. Springer, New York (2012)
Gonzalez, R.C.: Digital Image Processing. Pearson Education India (2009)
Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. Int. J. Comput. Vis. 61(3), 211–231 (2005)
Beauchemin, S.S., Barron, J.L.: The computation of optical flow. ACM Comput. Surv. (CSUR) 27(3), 433–466 (1995)
Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 500–513 (2011)
Brox, T., et al.: High accuracy optical flow estimation based on a theory for warping. In: Computer Vision-ECCV 2004, pp. 25–36. Springer, Heidelberg (2004)
Bouguet, J.-Y.: Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corp. 5, 1–10 (2001)
Grewal, M.S., Andrews, A.P.: Kalman Filtering: Theory and Practice Using MATLAB. Wiley, Hoboken (2011)
Ribeiro, M.I.: Kalman and extended kalman filters: concept, derivation and properties. Inst. Syst. Robot. 43 (2004)
Julier, S.J., Uhlmann, J.K.: New extension of the Kalman filter to nonlinear systems. In: AeroSense 1997. International Society for Optics and Photonics (1997)
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Wang, X., Yang, M., Gupta, L., Bai, Y. (2020). A New Technique for Laser Spot Detection and Tracking by Using Optical Flow and Kalman Filter. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_43
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DOI: https://doi.org/10.1007/978-3-030-17795-9_43
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