Detection and tracking of moving object using modified Background subtraction and Kalman filter

Jeevith S H


Moving Object detection and tracking is the major challenging issue in Computer vision, which plays a vital role in many applications like robotics, surveillance, navigation systems, militaries, environmental monitoring etc. There are several existing techniques, which has been used to detect and track the moving object in Surveillance system. Therefore it is necessary to develop new algorithm or modified algorithm which is robust to work in both day and night time. In this paper, modified BGS technique is proposed. The video is first converted to number of frames, then these frame are applied to modified background subtraction technique with adaptive threshold which gives detected object. Kalman filter technique is used for tracking the detected object. The experimental results shows this proposed method can efficiently and correctly detect and track the moving objects with less processing time which is compared with existing techniques.


Background Subtraction;Intersection over Union; Frames per second;False frame Detection


S.R.Balaji,Dr.S.Kartheyan,”A survey on Moving Object Tracking Using Image Processing”, International Conferenceon Intelligent Systems and Control (ISCO),2017

Shridevi.S.Vasekar, Sanjivani K. Shah,”Background Subtraction and Kalman Filter Algorithm for Object Tracking”, International conference on Recent Trends in Image Processing and Pattern Recognition RTIP2R 2018, CCIS 1035, pp. 194–202, 2019

Asad Abdul Malik, Amaad Khalil, Hameed Ullah Khan Object Detection and Tracking using Background Subtraction and Connected Component Labeling”, International Journal of Computer Applications (0975 – 8887) Volume 75– No.13, August 2013

Wenchao Liu He Chen , Long Ma “Moving object detection and tracking based on ZYNQ FPGA and ARM SOC”,IET”, International Radar Conference 2015,14-16 Oct. 2015.pp.1-4.

Trupti A. Chopkar, Shashikant Lahade, ”Real Time Detection of Moving Object Based On FPGA”, International Journal of Scientific Engineering and Research ,ISSN (Online): 2347-3878, Impact Factor (2014): 3.05,pp.37-41

G.Sindhura Bhargavi, B.Praveen Kumar, T. Kalyan,” Fast Background Subtraction Algorithm for Moving Object Detection & Tracking in FPGA”, International Journal of software and hardware research in Engineering, ISSN No 2347- 4890, vol.2, no.6, June 2014,pp.65-70

Poonam Gujrathi R, Arokia Priya ,P. Malathi,” Detecting Moving Object Using Background Subtraction Algorithm in FPGA”, Fourth International Conference on Advances in Computing and Communications (ICACC), 27-29 Aug. 2014,pp.117-120, INSPEC.14630878

Abutaleb M.M, Hamdy.A, Abuelwafa.M. E., Saad E. M,” FPGA-based object extraction based on multimodal sigma – delta background estimation”, 2nd International Conference on Computer, Control and Communication,2009. IC4 2009, Feb. 2009, pp. 1–7.

M. Saad, A. Hamdy and M. M. Abutaleb, “FPGA-Based implementation of a Low Cost and Area Real-Time Motion Detection”, 15th International Conference of Mixed Design MIXDES, pp. 249-254, Pozna´n, Poland, 2008.

S. Shantaiya, et al, “Multiple object tracking using kalman filter and optical flow,” European Journal of Advances in Engineering and Technology, vol. 2, no. 2, pp. 34 - 39, 2015

Butler.D,Sridharan.S,Bove V. M. ,”Real-time adaptive background segmentation”,IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP-2003), volume 3, vol. 3, Apr. 2003, pp.349–352

Li Q-Z., He D-X., Wang B,” Effective moving objects detection based on clustering background model for video surveillance”, Proceedings of the 2008 Congress on Image and Signal Processing, vol. 3, CISP ’08, Washington, DC, USA, 2008. IEEE Computer Society, pp. 656–660.

Appiah K., Hunter A,” A single-chip FPGA implementation of real-time adaptive background model”, IEEE International Conference on Field-Programmable Technology, 2005. Proceedings. Dec. 2005, pp. 95–102.

Butler. D, Sridharan S, Bove V. M. ,”Real-time adaptive background segmentation”,IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP-2003), volume 3, vol. 3, Apr. 2003, pp.349–352.

Haritaoglu I, Harwood D, Davis L. S,” W4: Who? when? where? what? A real time system for detecting and tracking people”, Third Face and Gesture Recognition Conference, Apr. 1998, pp. 222–227.

Ya Liu, Haizhou Ai, Guang-you Xu,” Moving object detection and tracking based on Background subtraction”, . International Society for Optics and Photonics In Multispectral Image Processing and Pattern Recognition, 2001 , pp.62–66

Stauffer C, Grimson W. E. L,” Adaptive background mixture models for real-time tracking”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, vol. 2, 1999, pp.637-663.

Alan J Lipton, Hironobu Fujiyoshi, and Raju S Patil.”Moving target classification and tracking from real-time video”, IEEE Proceedings in Applications of Computer Vision, 1998. WACV'98., Fourth IEEE Workshop on, pages 8–14., 1998.

Total views : 0 times

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

ISSN 2088-8708, e-ISSN 2722-2578