Robust Moving Object Detection and Tracking Framework Using Linear Phase FIR Filter

  • Tanmay SaxenaEmail author
  • Vikas Tripathi
  • Apoorv Chandola
  • Sarthak Garg
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 985)


Moving Object detection is a technique in computer vision in which multiple consecutive frames from a video are compared by applying various detection techniques to determine movement of an object. The most challenging task in motion detection is object tracking. In this paper we have proposed an effective framework for tracking down the moving object in which least square linear-phase Finite Impulse Response Filter is used for smoothing of image while optical flow estimation is used to calculate the motion between two images, median filter removes noise from a frame and motion vector estimation is used to find the variation in pixel movement using successive frames.


Object detection Object tracking Optical flow Least square Linear-Phase FIR Filter Median filter Motion vector estimation 


  1. 1.
    Verma, R.: A review of object detection and tracking methods. Int. J. Adv. Eng. Res. Dev. 4(10), 569–578 (2017)Google Scholar
  2. 2.
    Abdelali, H.A., Essannouni, F., Aboutajdine, D.: Object tracking in video via particle filter. Int. J. Intell. Eng. Inf. 4(3–4), 340–353 (2016)Google Scholar
  3. 3.
    Shantaiya, S., Verma, K., Mehta, K.: Multiple object tracking using Kalman filter and optical flow. Eur. J. Adv. Eng. Technol. 2(2), 34–39 (2015)Google Scholar
  4. 4.
    Vekariya, D., Shah, H.R., Sodha, N.: Implementation of object tracking using camera. Int. J. Adv. Eng. Res. Dev. 2(6), 225–237 (2015)Google Scholar
  5. 5.
    Charadva, M.J., Sejpal, R.V., Sarwade, N.P.: A study of motion detection method for smart home system. Int. J. Innov. Res. Adv. Eng. (IJIRAE) 1(5), 148–151 (2014)Google Scholar
  6. 6.
    Li, A., Yan, S.: Object tracking with only background cues. IEEE Trans. Circ. Syst. Video Technol. 24(11), 1911–1919 (2014)CrossRefGoogle Scholar
  7. 7.
    Murugan, A.S., Devi, K.S., Sivaranjani, A., Srinivasan, P.: A study on various methods used for video summarization and moving object detection for video surveillance applications. Multimed. Tools Appl. 77(18), 23273–23290 (2018)CrossRefGoogle Scholar
  8. 8.
    Kale, K., Pawar, S., Dhulekar, P.: Moving object tracking using optical flow and motion vector estimation. In: 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), pp. 1–6. IEEE, Noida (2015)Google Scholar
  9. 9.
    Walker, J., Gupta, A., Hebert, M.: Dense optical flow prediction from a static image. In: International Conference on Computer Vision, pp. 2443–2451. IEEE, Araucano Park (2015)Google Scholar
  10. 10.
    Wang, Z., Yang, X.: Moving target detection and tracking based on pyramid Lucas-Kanade optical flow. In: 3rd International Conference on Image, Vision and Computing (ICIVC), pp. 66–69. IEEE, Chongqing (2018)Google Scholar
  11. 11.
    Thota, S.D., Vemulapalli, K.S., Chintalapati, K., Gudipudi, P.S.S.: Int. J. Eng. Trends Technol. 4(10), 4507–4511 (2013)Google Scholar
  12. 12.
    Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: video structure comparison for recognition of complex human activities. In: IEEE International Conference on Computer Vision (ICCV), pp. 1593–1600. IEEE, Kyoto (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Tanmay Saxena
    • 1
    Email author
  • Vikas Tripathi
    • 1
  • Apoorv Chandola
    • 1
  • Sarthak Garg
    • 1
  1. 1.Graphic Era (Deemed to be University)DehradunIndia

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