Motion Detection by Microcontroller for Panning Cameras

  • Jesús Benito-PicazoEmail author
  • Ezequiel López-Rubio
  • Juan Miguel Ortiz-de-Lazcano-Lobato
  • Enrique Domínguez
  • Esteban J. Palomo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)


Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, because of the high quality of the achieved segmentation. However, real time requirements and high costs prevent most of the algorithms proposed in literature to exploit the background difference with panning cameras in real world applications. This paper presents a new algorithm to detect moving objects within a scene acquired by panning cameras. The algorithm for motion detection is implemented on a Raspberry Pi microcontroller, which enables the design and implementation of a low-cost monitoring system.


Foreground detection Background modeling Probabilistic self-organizing maps Background features 



This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2014-53465-R, project name Video surveillance by active search of anomalous events. It is also partially supported by the Autonomous Government of Andalusia (Spain) under projects TIC-6213, project name Development of Self-Organizing Neural Networks for Information Technologies; and TIC-657, project name Self-organizing systems and robust estimators for video surveillance. Finally, it is partially supported by the Autonomous Government of Extremadura (Spain) under the project IB13113. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.


  1. 1.
    Adnan, L., Yussoff, Y., Johar, H., Baki, S.: Energy-saving street lighting system based on the waspmote mote. Jurnal Teknologi 76(4), 55–58 (2015)CrossRefGoogle Scholar
  2. 2.
    Boult, T., Gao, X., Micheals, R., Eckmann, M.: Omni-directional visual surveillance. Image Vis. Comput. 22(7), 515–534 (2004)CrossRefGoogle Scholar
  3. 3.
    Chen, G., St-Charles, P., Bouachir, W., Bilodeau, G., Bergevin, R.: Reproducible evaluation of pan-tilt-zoom tracking. In: Proceedings - International Conference on Image Processing (ICIP), pp. 2055–2059, December 2015Google Scholar
  4. 4.
    Ding, C., Song, B., Morye, A., Farrell, J., Roy-Chowdhury, A.: Collaborative sensing in a distributed PTZ camera network. IEEE Trans. Image Process. 21(7), 3282–3295 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Dobrzynski, M.K., Pericet-Camara, R., Floreano, D.: Vision tape-a flexible compound vision sensor for motion detection and proximity estimation. IEEE Sens. J. 12(5), 1131–1139 (2012)CrossRefGoogle Scholar
  6. 6.
    Fung, V., Bosch, J.L., Roberts, S.W., Kleissl, J.: Cloud shadow speed sensor. Atmos. Measur. Tech. 7(6), 1693–1700 (2014)CrossRefGoogle Scholar
  7. 7.
    Ling, C.X., Huang, J., Zhang, H.: AUC: a statistically consistent and more discriminating measure than accuracy. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 519–524 (2003)Google Scholar
  8. 8.
    Micheloni, C., Rinner, B., Foresti, G.: Video analysis in pan-tilt-zoom camera networks. IEEE Signal Process. Mag. 27(5), 78–90 (2010)CrossRefGoogle Scholar
  9. 9.
    Nissen, S.: Fast Artificial Neural Network (2016). Accessed 10 Jan 2017
  10. 10.
    Ortega-Zamorano, F., Molina-Cabello, M.A., López-Rubio, E., Palomo, E.J.: Smart motion detection sensor based on video processing using self-organizing maps. Expert Syst. Appl. 64, 476–489 (2016)CrossRefGoogle Scholar
  11. 11.
    Papadimitriou, K., Dollas, A., Sotiropoulos, S.N.: Low-cost real-time 2-D motion detection based on reconfigurable computing. IEEE Trans. Instrum. Meas. 55(6), 2234–2243 (2006)CrossRefGoogle Scholar
  12. 12.
    Parker, C.: An analysis of performance measures for binary classifiers. In: Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 517–526 (2011)Google Scholar
  13. 13.
    Song, K.T., Tai, J.C.: Dynamic calibration of pan-tilt-zoom cameras for traffic monitoring. IEEE Trans. Syst. Man Cybern. B Cybern. 36(5), 1091–1103 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jesús Benito-Picazo
    • 1
    Email author
  • Ezequiel López-Rubio
    • 1
  • Juan Miguel Ortiz-de-Lazcano-Lobato
    • 1
  • Enrique Domínguez
    • 1
  • Esteban J. Palomo
    • 1
    • 2
  1. 1.Department of Computer Languages and Computer ScienceUniversity of MálagaMálagaSpain
  2. 2.School of Mathematical Sciences and Information TechnologyUniversity of Yachay TechSan Miguel de UrcuquíEcuador

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