Online Object Trajectory Classification Using FPGA-SoC Devices

  • Pranjali Shinde
  • Pedro MachadoEmail author
  • Filipe N. Santos
  • T. M. McGinnity
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)


Real time classification of objects using computer vision techniques are becoming relevant with emergence of advanced perceptions systems required by, surveillance systems, industry 4.0 robotics and agricultural robots. Conventional video surveillance basically detects and tracks moving object whereas there is no indication of whether the object is approaching or receding the camera (looming). Looming detection and classification of object movements aids in knowing the position of the object and plays a crucial role in military, vehicle traffic management, robotics, etc. To accomplish real-time object trajectory classification, a contour tracking algorithm is necessary. In this paper, an application is made to perform looming detection and to detect imminent collision on a system-on-chip field-programmable gate array (SoC- FPGA) hardware. The work presented in this paper was designed for running in Robotic platforms, Unmanned Aerial Vehicles, Advanced Driver Assistance System, etc. Due to several advantages of SoC-FPGA the proposed work is performed on the hardware. The proposed work focusses on capturing images, processing, classifying the movements of the object and issues an imminent collision warning on-the-fly. This paper details the proposed software algorithm used for the classification of the movement of the object, simulation of the results and future work.


SoC-FPGA Computer vision Colour detection Contour tracking Trajectory detection Object tracking 


  1. 1.
    Guennouni, S., Ahaitouf, A., Mansouri, A.: Multiple object detection using openCV on an embedded platform. In: 2014 Third IEEE International Colloquium in Information Science and Technology (CIST), pp. 374–377, October 2014Google Scholar
  2. 2.
    Intel FPGA devices. Accessed 06 June 2018
  3. 3.
    Machado, P., Wade, J., McGinnity, T.M.:. Si elegans: modeling the C. elegans nematode nervous system using high performance FPGAS. In: Londral, R.A., Encarnação, P. (eds.) Advances in Neurotechnology, Electronics and Informatics, Chap. Si elegans, 12th edn., pp. 31–45. Springer, Heidelberg (2016)Google Scholar
  4. 4.
    Tang, J.W., Shaikh-Husin, N., Sheikh, U.U., Marsono, M.N.: FPGA-based real-time moving target detection system for unmanned aerial vehicle application. Int. J. Reconfigurable Comput. (2016)Google Scholar
  5. 5.
    Firmanda, D., Pramadihanto, D.: Computer vision based analysis for cursor control using object tracking and color detection. In: 2014 Seventh International Symposium on Computational Intelligence and Design (2014)Google Scholar
  6. 6.
    Prasad, S., Sinha, S.: Real-time object detection and tracking in an unknown environment. In: 2011 World Congress on Information and Communication Technologies, pp. 1056–1061, December 2011Google Scholar
  7. 7.
    Uke, N.J., Futane, P.R.: Efficient method for detecting and tracking moving objects in video. In: 2016 IEEE International Conference on Advances in Electronics, ICAECCT, Communication and Computer Technology, p. 2017 (2016)Google Scholar
  8. 8.
    Pea-Gonzlez, R.H., Nuo-Maganda, M.A.: Computer vision based real-time vehicle tracking and classification system. In: 2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 679–682, August 2014Google Scholar
  9. 9.
    Gajbhiye, S.D., Gundewar, P.P.: A real-time color-based object tracking and occlusion handling using arm cortex-a7. In: 2015 Annual IEEE India Conference (INDICON), pp. 1–6, December 2015Google Scholar
  10. 10.
    Nieto, M., Otaegui, O., Vélez, G., Ortega, J.D., Cortés, A.: On creating vision-based advanced driver assistance systems. In: IET Intelligent Transport Systems (2015)Google Scholar
  11. 11.
    Stein, F.: The challenge of putting vision algorithms into a car. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2012)Google Scholar
  12. 12.
    Appiah, K., Meng, H., Hunter, A., Dickinson, P.: Binary histogram based split/merge object detection using FPGAs. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp. 45–52, June 2010Google Scholar
  13. 13.
    Chayeb, A., Ouadah, N., Tobal, Z., Lakrouf, M., Azouaoui,O.: HOG based multi-object detection for urban navigation. In: 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 (2014)Google Scholar
  14. 14.
    Wang, Z., Song, H., Xiao, H., He, W., Gu, J., Yuan, K.: A real-time small moving object detection system based on infrared image (2014)Google Scholar
  15. 15.
    Saravanan, G., Yamuna, G., Nandhini, S.: Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 0462–0466, April 2016Google Scholar
  16. 16.
    Xue, T., Wang, Y., Qi, Y.: Multi-feature fusion based GMM for moving object and shadow detection. In: International Conference on Signal Processing Proceedings, ICSP (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pranjali Shinde
    • 1
  • Pedro Machado
    • 2
    Email author
  • Filipe N. Santos
    • 1
  • T. M. McGinnity
    • 2
  1. 1.INESC TEC Campus da Faculdade de Engenharia da Universidade do PortoPortoPortugal
  2. 2.Computational Neuroscience and Cognitive Robotics LaboratoryNottingham Trent UniversityNottinghamUK

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