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Research Article
Exploring Image Processing Based Object Detection & Tracking Techniques in Videos: A Concise Overview
Priya Jain1
Meenakshi Arora2
Rohini Sharma3
Priya Jain, Meenakshi Arora, Rohini Sharmam“‘Exploring Image Processing Based Object Detection & Tracking Techniques in Videos: A Concise Overview”, IJIRE-V5I03-180-185
Published Online: May-June 2024
Pages: 180-185
Cite this article
No DOIReferences
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and tracking of breast surface deformations,” IEEE Trans. Biomed. Eng., 2023.
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21. P. Li, H. Zhao, P. Liu, and F. Cao, “Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving,” inEuropean Conference on Computer Vision, 2020, pp. 644–660.
22. Y. Sun, L. Zheng, Y. Yang, Q. Tian, and S. Wang, “Beyond part models: Person retrieval with refined part pooling (and a strong
convolutional baseline),” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 480–496.
23. R. Brunelli and T. Poggiot, “Template matching: Matched spatial filters and beyond,” Pattern Recognit., vol. 30, no. 5, pp. 751–768,
1997.
24. Y. Wu, J. Lim, and M.-H. Yang, “Online object tracking: A benchmark,” in Proceedings of the IEEE conference on computer vision
and pattern recognition, 2013, pp. 2411–2418.
25. J. Munkres, “Algorithms for the assignment and transportation problems,” J. Soc. Ind. Appl. Math., vol. 5, no. 1, pp. 32–38, 1957.
26. B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in IJCAI’81: 7th
international joint conference on Artificial intelligence, 1981, vol. 2, pp. 674–679.
27. A. P. Witkin, “Scale-space filtering,” in Readings in computer vision, Elsevier, 1987, pp. 329–332.
28. Y. Cheng, F. Lu, and X. Zhang, “Appearance-based gaze estimation via evaluation-guided asymmetric regression,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 100–115.
25105, 2022.
2. C. A. M. Busch, K. A. Stol, and W. van der Mark, “Dynamic tree branch tracking for aerial canopy sampling using stereo vision,”
Comput. Electron. Agric., vol. 182, p. 106007, 2021.
3. X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick, and A. Van Den Hengel, “A survey of appearance models in visual object tracking,” ACM
Trans. Intell. Syst. Technol., vol. 4, no. 4, pp. 1–48, 2013.
4. L. Kalake, W. Wan, and L. Hou, “Analysis based on recent deep learning approaches applied in real-time multi-object tracking: a
review,” IEEE Access, vol. 9, pp. 32650–32671, 2021.
5. M. Mandal and S. K. Vipparthi, “An empirical review of deep learning frameworks for change detection: Model design, experimental
frameworks, challenges and research needs,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 7, pp. 6101–6122, 2021.
6. S. Guo et al., “A review of deep learning-based visual multi-object tracking algorithms for autonomous driving,” Appl. Sci., vol. 12,
no. 21, p. 10741, 2022.
7. T. Kriechbaumer, K. Blackburn, T. P. Breckon, O. Hamilton, and M. Rivas Casado, “Quantitative evaluation of stereo visual odometry
for autonomous vessel localisation in inland waterway sensing applications,” Sensors, vol. 15, no. 12, pp. 31869–31887, 2015.
8. A. Geiger, J. Ziegler, and C. Stiller, “Stereoscan: Dense 3d reconstruction in real-time,” in 2011 IEEE intelligent vehicles symposium
(IV), 2011, pp. 963–968.
9. R. E. Kalman, “A new approach to linear filtering and prediction problems,” 1960.
10. F. Steinbrücker, J. Sturm, and D. Cremers, “Real-time visual odometry from dense RGB-D images,” in 2011 IEEE international
conference on computer vision workshops (ICCV Workshops), 2011, pp. 719–722.
11. M. D. Jenkins, P. Barrie, T. Buggy, and G. Morison, “Extended fast compressive tracking with weighted multi-frame template matching
for fast motion tracking,” Pattern Recognit. Lett., vol. 69, pp. 82–87, 2016.
12. M.-C. Chuang, J.-N. Hwang, K. Williams, and R. Towler, “Tracking live fish from low-contrast and low-frame-rate stereo videos,”
IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 1, pp. 167–179, 2014.
13. J. Yang, R. Xu, Z. Ding, and H. Lv, “3D character recognition using binocular camera for medical assist,” Neurocomputing, vol. 220,
pp. 17–22, 2017.
14. J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell., no. 6, pp. 679–698, 1986.
15. V. A. Deepambika and M. A. Rahman, “Illumination Invariant Motion Detection and Tracking Using SMDWT and a Dense Disparity‐
Variance Method,” J. Sensors, vol. 2018, no. 1, p. 1354316, 2018.
16. W. L. Richey, J. S. Heiselman, M. J. Ringel, I. M. Meszoely, and M. I. Miga, “Soft tissue monitoring of the surgical field: detection
and tracking of breast surface deformations,” IEEE Trans. Biomed. Eng., 2023.
17. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” Int. J. Rob. Res., vol. 32, no. 11, pp. 1231–
1237, 2013.
18. P. F. Alcantarilla, A. Bartoli, and A. J. Davison, “KAZE features,” in Computer Vision–ECCV 2012: 12th European Conference on
Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI 12, 2012, pp. 214–227.
19. S. Feng et al., “VIMOT: A Tightly-Coupled Estimator for Stereo Visual-Inertial Navigation and Multi-Object Tracking,” IEEE Trans. Instrum. Meas., 2023.
20. G. D. Forney, “The viterbi algorithm,” Proc. IEEE, vol. 61, no. 3, pp. 268–278, 1973.
21. P. Li, H. Zhao, P. Liu, and F. Cao, “Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving,” inEuropean Conference on Computer Vision, 2020, pp. 644–660.
22. Y. Sun, L. Zheng, Y. Yang, Q. Tian, and S. Wang, “Beyond part models: Person retrieval with refined part pooling (and a strong
convolutional baseline),” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 480–496.
23. R. Brunelli and T. Poggiot, “Template matching: Matched spatial filters and beyond,” Pattern Recognit., vol. 30, no. 5, pp. 751–768,
1997.
24. Y. Wu, J. Lim, and M.-H. Yang, “Online object tracking: A benchmark,” in Proceedings of the IEEE conference on computer vision
and pattern recognition, 2013, pp. 2411–2418.
25. J. Munkres, “Algorithms for the assignment and transportation problems,” J. Soc. Ind. Appl. Math., vol. 5, no. 1, pp. 32–38, 1957.
26. B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in IJCAI’81: 7th
international joint conference on Artificial intelligence, 1981, vol. 2, pp. 674–679.
27. A. P. Witkin, “Scale-space filtering,” in Readings in computer vision, Elsevier, 1987, pp. 329–332.
28. Y. Cheng, F. Lu, and X. Zhang, “Appearance-based gaze estimation via evaluation-guided asymmetric regression,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 100–115.
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