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Real-time tracking of moving objects through efficient scale space adaptation and normalized correlation filtering

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Abstract

The field of real-time mobile object tracking is a crucial aspect of computer vision. Despite numerous algorithms proposed for efficient tracking, the high computational complexity presents challenges in achieving real-time performance. This paper presents a novel approach by introducing an adaptive search region proposal block that works in tandem with Mean-Shift and Unscented Kalman Filter. The block efficiently searches the region of the estimated object location. The dynamic changes in the appearance and size of a moving target make tracking difficult, but the proposed Multi-scale Template Matching technique addresses this challenge by utilizing the Normalized Cross-Correlation method in the adaptive search region. This optimization results in a reduced computational complexity and an increased frame rate of 53.4 FPS. Comparisons with various state-of-the-art trackers show that the proposed algorithm achieves the best results in terms of precision, success rate, and object tracking error.

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References

  1. Matsuzaki, T., Kameda, H., Tsujimichi, S., Kosuge, K.: Maneuvering target tracking using constant velocity and constant angular velocity model. In: Smc 2000 conference proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions’ cat. no. 0, vol. 5, pp. 3230–3234 (2000)

  2. Gunjal, P.R., Gunjal, B.R., Shinde, H.A., Vanam, S.M., Aher, S.S.: Moving object tracking using kalman filter. In: 2018 International Conference on Advances in Communication and Computing Technology (ICACCT), pp. 544–547, (2018)

  3. Li, Q., Li, R., Ji, K., Dai, W.: Kalman filter and its application. In: 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), pp. 74–77 (2015)

  4. Cho, J.U., Jin, S.H., Pham, X.D., Jeon, J.W., Byun, J.E., Kang, H.: A real-time object tracking system using a particle filter. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2822–2827 (2006)

  5. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17(1–3), 185–203 (1981)

    Article  Google Scholar 

  6. Sibiryakov, A.: Fast and High-Performance Template Matching Method. pp. 1417–1424, (2011)

  7. O’Shea, K., Nash, R.: An introduction to convolutional neural networks. (2015) CoRR, arXiv:1511.08458

  8. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9, 1735–80 (1997)

    Article  Google Scholar 

  9. Avidan, S.: Support vector tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(8), 1064–1072 (2004)

    Article  Google Scholar 

  10. Wang, H., Qiu, H., Li, W.: Nonconvex dictionary learning based visual tracking method. Signal Processing 172, 107535 (2020)

    Article  Google Scholar 

  11. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)

    Article  Google Scholar 

  12. Pale-Ramon, E.G., Morales-Mendoza, L.J., González-Lee, M., Ibarra-Manzano, O.G., Ortega-Contreras, J.A., Shmaliy, Y.S.: Improving visual object tracking using general ufir and kalman filters under disturbances in bounding boxes. IEEE Access 11, 57905–57915 (2023)

    Article  Google Scholar 

  13. Mehmood, K., Jalil, A., Ali, A., Khan, B., Murad, M., Khan, W.U., He, Y.: Context-aware and occlusion handling mechanism for online visual object tracking. Electronics 10(1) (2021)

  14. Yu, X., Zhang, Y., Wu, H., Wang, A.: An improved unscented kalman filtering combined with feature triangle for head position tracking. Electronics, 12(12), (2023)

  15. Kumar, M., Mondal, S.: State estimation of radar tracking system using a robust adaptive unscented kalman filter. Aerospace Systems 6(2), 375–381 (2023)

    Article  Google Scholar 

  16. Collins, R.T.: Mean-shift blob tracking through scale space. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., vol. 2, pp. II–234, (2003)

  17. Chen, X., Wang, X., Xuan, J.: Tracking multiple moving objects using unscented kalman filtering techniques. (2018) arXiv preprint arXiv:1802.01235

  18. Ali, A., Ghosh, A., Chaudhuri, S.S.: Determination of optimum dynamic threshold for visual object tracker. In: International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), Rajshahi, Bangladesh, IEEE Conference Record 53878 (2021)

  19. Gundogdu, E., Alatan, A.A.: Good features to correlate for visual tracking. (2017) CoRR, arXiv:1704.06326

  20. Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: European Conference on Computer Vision, pp. 234–247. Springer, Berlin (2008)

  21. Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: automatic detection of tracking failures. In: 2010 20th International Conference on Pattern Recognition, pp. 2756–2759 (2010)

  22. Zhang, K., Zhang, L., Yang, M.-H.: Fast compressive tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(10), 2002–2015 (2014)

    Article  Google Scholar 

  23. Yao, Y., Wu, X., Zhang, L., Shan, S., Zuo, W.: Joint representation and truncated inference learning for correlation filter based tracking. (2018) CoRR, arXiv:1807.11071

  24. Wang, X., Han, T.X., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: 2009 IEEE 12th international conference on computer vision, pp. 32–39. IEEE (2009)

  25. Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1830–1837 (2012)

  26. Chu, H., Xie, Z., Nie, X., Li, Z., Li, X.: Particle filter target tracking method optimized by improved mean shift. In: 2013 IEEE International Conference on Information and Automation (ICIA), pp. 991–994 (2013)

  27. Comaniciu, D., Ramesh, V.: Mean shift and optimal prediction for efficient object tracking. In Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), vol. 3, pp. 70–73 (2000)

  28. Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: a benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2013)

  29. Mueller, M., Smith, N., Ghanem, B.: Context-aware correlation filter tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017)

  30. Shin, J., Kim, H., Kim, D., Paik, J.: Fast and robust object tracking using tracking failure detection in kernelized correlation filter. Applied Sciences, 10(2), (2020)

  31. Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.H.: Fast visual tracking via dense spatio-temporal context learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8693 LNCS(PART 5):127–141, (2014). 13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014

  32. Zhang, Y., Yang, Y., Zhou, W., Shi, L., Li, D.: Motion-aware correlation filters for online visual tracking. Sensors 18, 3937 (2018)

    Article  Google Scholar 

  33. Mehmood, K., Ali, A., Jalil, A., Khan, B., Cheema, K.M., Murad, M., Milyani, A.H.: Efficient online object tracking scheme for challenging scenarios. Sensors, 21(24), (2021)

  34. Khan, B., Ali, A., Jalil, A., Mehmood, K., Murad, M., Awan, H.: Afam-pec: adaptive failure avoidance tracking mechanism using prediction-estimation collaboration. IEEE Access 8, 149077–149092 (2020)

    Article  Google Scholar 

  35. Li, Z., Tao, R., Gavves, E., Snoek, C.G.M., Smeulders, A.W.M.: Tracking by natural language specification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017)

  36. Luo, Y., Xu, M., Yuan, C., Cao, X., Zhang, L., Xu, Y., Wang, T., Feng, Q.: Siamsnn: Siamese spiking neural networks for energy-efficient object tracking. In: International Conference on Artificial Neural Networks, (2020)

  37. Xu, Y., Wang, Z., Li, Z., Yuan, Y., Yu, G.: Siamfc++: towards robust and accurate visual tracking with target estimation guidelines. Proceedings of the AAAI Conference on Artificial Intelligence 34, 12549–12556 (2020)

    Article  Google Scholar 

  38. Feng, Q., Ablavsky, V., Bai, Q., Sclaroff, S.: Siamese natural language tracker: tracking by natural language descriptions with siamese trackers. pp. 5847–5856 (2021)

  39. Wang, X., Shu, X., Zhang, Z., Jiang, B., Wang, Y., Tian, Y., Wu, F.: Towards more flexible and accurate object tracking with natural language: algorithms and benchmark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13763–13773, (2021)

  40. Feng, Q., Ablavsky, V., Bai, Q., Li, G., Sclaroff, S.: Real-time visual object tracking with natural language description. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2020)

  41. Xiang, S., Zhang, T., Jiang, S., Han, Y., Zhang, Y., Du, C., Guo, X., Yu, L., Shi, Y., Hao, Y.: Spiking siamfc++: deep spiking neural network for object tracking, (2022)

  42. Guo, M., Zhang, Z., Fan, H., Jing, L.: Divert more attention to vision-language tracking. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds) Advances in Neural Information Processing Systems, vol. 35, pp. 4446–4460. Curran Associates, Inc. (2022)

  43. Bao, J., Yan, M., Yang, Y., Chen, K.: Siamffn: Siamese feature fusion network for visual tracking. Electronics, 12(7), (2023)

  44. Zhou, L., Zhou, Z., Mao, K., He, Z.: Joint visual grounding and tracking with natural language specification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 23151–23160, (2023)

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The authors received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Department of Electronics and Tele-Communication Engineering, 188, Raja S.C. Mallick Road, Kolkata, West Bengal, 700032, India

    Asfak Ali, Avra Ghosh & Sheli Sinha Chaudhuri

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  1. Asfak Ali
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  2. Avra Ghosh
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  3. Sheli Sinha Chaudhuri
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Contributions

Author AA conceived the idea presented and developed the theory. He also performed the computations and prepared figures and tables and datasets. Author AG verified the analytical methods. Author SSC encouraged authors 1 and 2 to investigate the experiment procedure and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Asfak Ali.

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Ali, A., Ghosh, A. & Chaudhuri, S.S. Real-time tracking of moving objects through efficient scale space adaptation and normalized correlation filtering. SIViP 18, 679–689 (2024). https://doi.org/10.1007/s11760-023-02758-x

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