Skip to main content
SpringerLink
Log in

Multi-object Detection and Tracking (MODT) Machine Learning Model for Real-Time Video Surveillance Systems

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Recently, video surveillance has garnered considerable attention in various real-time applications. Due to advances in the field of machine learning, numerous techniques have been developed for multi-object detection and tracking (MODT). This paper introduces a new MODT methodology. The proposed method uses an optimal Kalman filtering technique to track the moving objects in video frames. The video clips were converted based on the number of frames into morphological operations using the region growing model. After distinguishing the objects, Kalman filtering was applied for parameter optimization using the probability-based grasshopper algorithm. Using the optimal parameters, the selected objects were tracked in each frame by a similarity measure. Finally, the proposed MODT framework was executed, and the results were assessed. The experiments showed that the MODT framework achieved maximum detection and tracking accuracies of 76.23% and 86.78%, respectively. The results achieved with Kalman filtering in the MODT process are compared with the results of previous studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. H.A. Abdelali, F. Essannouni, L. Essannouni, D. Aboutajdine, Algorithm for moving object detection and tracking in video sequence using color feature. in 2014 Second World Conference on Complex Systems (WCCS) (2014), pp. 690–693

  2. R. Assaf, A. Goupil, V. Vrabie, T. Boudier, M. Kacim, Persistent homology for object segmentation in multidimensional grayscale images. Pattern Recogn. Lett. 112, 277–284 (2018)

    Article  Google Scholar 

  3. K. Chen, J. Wang, S. Yang, X. Zhang, Y. Xiong, C.C. Loy, D. Lin, Optimizing video object detection via a scale-time lattice. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 7814–7823

  4. A.A. Ewees, M.A. Elaziz, E.H. Houssein, Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst. Appl. 112, 1–31 (2018)

    Article  Google Scholar 

  5. Q. Feng, B. Gao, P. Lu, W.L. Woo, Y. Yang, Y. Fan, X. Qiu, L. Gu, Automatic seeded region growing for thermography debonding detection of CFRP. NDT E Int. 99, 36–49 (2018)

    Article  Google Scholar 

  6. S.M.A. Hasan, K. Ko, Depth edge detection by image-based smoothing and morphological operations. J. Comput. Des. Eng. 3(3), 191–197 (2016)

    Google Scholar 

  7. https://motchallenge.net/vis/PETS09-S2L1

  8. E. Hamuda, B. Mc Ginley, M. Glavin, E. Jones, Improved image processing-based crop detection using Kalman filtering and the Hungarian algorithm. Comput. Electron. Agric. 148, 37–44 (2018)

    Article  Google Scholar 

  9. I.A. Iswanto, B. Li, Visual object tracking based on mean-shift and particle-Kalman filter. Proc. Comput. Sci. 116, 587–595 (2017)

    Article  Google Scholar 

  10. S. Kuppuswamy, B. Panchanathan, Similar object detection and tracking in H. 264 compressed video using modified local self similarity descriptor and particle filtering. Int. J. Intell. Eng. Syst. 10(5), 95–104 (2017)

    Google Scholar 

  11. S. Li, C. Zeng, Y. Fu, S. Liu, Optimizing multi-graph learning based salient object detection. Signal Process. Image Commun. 55, 93–105 (2017)

    Article  Google Scholar 

  12. J. Luo, H. Chen, Y. Xu, H. Huang, X. Zhao, An improved grasshopper optimization algorithm with application to financial stress prediction. Appl. Math. Model. 64, 654–668 (2018)

    Article  MathSciNet  Google Scholar 

  13. L. Liang-qun, Z. Xi-yang, L. Zong-xiang, X. Wei-xin, Fuzzy logic approach to visual multi-object tracking. Neurocomputing 281, 139–151 (2018)

    Article  Google Scholar 

  14. E. Maekawa, S. Goto, Examination of a tracking and detection method using compressed domain information, in 2013 IEEE Picture Coding Symposium (PCS) (2013), pp. 141–144

  15. T. Mahalingam, M. Subramoniam, A robust single and multiple moving object detection, tracking and classification. Applied Comput. Inf. (2018). https://doi.org/10.1016/j.aci.2018.01.001

    Article  Google Scholar 

  16. S.S. Mohamed, N.M. Tahir, R. Adnan, Mohamed, background modelling and background subtraction performance for object detection, in 2010 6th IEEE International Colloquium on Signal Processing & its Applications (2010), pp. 1–6

  17. S.P. Patil, Techniques and methods for detection and tracking of moving object in a video. J. Innov. Res. Comput. Commun. Eng. 4(5), 8116–8120 (2015)

    Google Scholar 

  18. S. Pollnow, N. Pilia, G. Schwaderlapp, A. Loewe, O. Dössel, G. Lenis, An adaptive spatio-temporal Gaussian filter for processing cardiac optical mapping data. Comput. Biol. Med. 102, 1–39 (2018)

    Article  Google Scholar 

  19. K.R. Reddy, K.H. Priya, N. Neelima, Object detection and tracking: a survey, in 2015 IEEE International Conference on Computational Intelligence and Communication Networks (CICN) (2015), , pp. 418–421

  20. K.S. Ray, S. Chakraborty, An Efficient Approach for Object Detection and Tracking of Objects in a Video with Variable Background (2017). arXiv preprint arXiv:1706.02672

  21. P.K. Sahoo, P. Kanungo, S. Mishra, A fast valley-based segmentation for detection of slowly moving objects. Signal Image Video Process. 7, 1–8 (2018)

    Article  Google Scholar 

  22. K. Srinivasan, P. Balamurugan, Object detection and movement tracking in H. 264 compressed video based on optimal particle filtering technique. J. Comput. Theor. Nanosci. 13(11), 8171–8181 (2016)

    Article  Google Scholar 

  23. M. Teutsch, W. Krüger, Detection, segmentation, and tracking of moving objects in UAV videos. in 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS) (2012), pp. 313–318

  24. Y. Tan, L. Liu, Q. Liu, J. Wang, X. Ma, H. Ni, Automatic breast DCE-MRI segmentation using compound morphological operations. Fourth IEEE Int. Conf. BMEI 1, 147–150 (2011)

    Google Scholar 

  25. K.K. Verma, P. Kumar, A. Tomar, Analysis of moving object detection and tracking in video surveillance system, in 2015 2nd IEEE International Conference on Computing for Sustainable Global Development (INDIACom) (2015), pp. 1758–1762

  26. J. Xu, L. Zhao, S. Zhang, C. Gong, J. Yang, Multi-task learning for object keypoints detection and classification. Pattern Recognit. Lett. 13, 1–10 (2017)

    Google Scholar 

  27. Z. Zhou, D. Wu, Z. Zhu, Object tracking based on Kalman particle filter with LSSVR. Optik Int. J. Light Electron Opt. 127(2), 613–619 (2016)

    Article  Google Scholar 

  28. D. Zhou, V. Frémont, B. Quost, Y. Dai, H. Li, Moving object detection and segmentation in urban environments from a moving platform. Image Vis. Comput. 68, 76–87 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Mansoura University, Mansoura, Egypt

    M. Elhoseny

Authors
  1. M. Elhoseny
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Elhoseny.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elhoseny, M. Multi-object Detection and Tracking (MODT) Machine Learning Model for Real-Time Video Surveillance Systems. Circuits Syst Signal Process 39, 611–630 (2020). https://doi.org/10.1007/s00034-019-01234-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-019-01234-7

Keywords

Search

Navigation