An Overview of Object Tracing Techniques in Videos and Images
Keywords:
Computer Vision
Abstract
The object Tracing is a growing area of image processing research with numerous important applications. Using object tracking, it is possible to accurately and automatically identify objects in films. This is accomplished by creating models for each individual object and tracking its motion as it moves around the screen or through various camera angles. For tracing objects, various techniques have been suggested. These largely differ from one another. This article aims to categorize Tracing techniques into useful groups and offers thorough justifications for distinctive approaches in each group. This paper reviews different research work done in the field of object tracking. In this article, we have examined the object tracing system and related work. The summary includes several approaches, strategies, types of photos, and outcomes. an overview of the papers that have been reviewed and the best findings are then given.
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References
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2. Jasdeep Singh, Kirti Bhatia, Shalini Bhadola, Rohini Sharma, An Analytical Survey on Dimension Reduction Based Face Recognition Systems, International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management (IJMRSETM), Volume 9, Issue 7, July 2022, pp.1493-1498.
3. Akash Dagar, Princy, Kirti Bhatia, Rohini Sharma, Demosaicing Techniques Thorough Analysis, International Journal of Innovative Research in Science, Engineering, and Technology (IJIRSET), Volume 11, Issue 7, July 2022, pp.9902-9907.
4. Jyoti, Kirti Bhatia, Shalini Bhadola, Rohini Sharma, An Analysis of Facsimile Demosaicing Procedures, International journal of Innovative Research in computer and communication engineering, Vol-08, Issue-07, July 2020.
5. Deepak Dahiya, Kirti Bhatia, Rohini Sharma, Shalini Bhadola, A Deep Overview on Image Denoising Approaches, International Journal of Innovative Research in Computer and Communication Engineering, Volume 9, Issue 7, July 2021.
6. Nakul Nalwa, Shalini Bhadola, Kirti Bhatia, Rohini Sharma, A Detailed Study on People Tracking Methodologies in Different Scenarios, International Journal of Innovative Research in Computer and Communication Engineering, Volume 10, Issue 6, June 2022.
7. Mahesh Kumar Attri, Kirti Bhatia, Shalini Bhadola, Rohini Sharma, An Image Sharpening and Smoothing Approaches Analysis, International Journal of Innovative Research in Computer and Communication Engineering, Volume 10, Issue 6, June 2022, pp 5573-5580.
8. Yogita, Shalini Bhadola, Kirti Bhatia, Rohini Sharma, A Deep Overview of Image Inpainting Approaches, International Journal of Innovative Research in Science, Engineering and Technology, Volume 11, Issue 6, June 2022, pp. 8744-8749.
9. Ojha, S. and Sakhare, S. (2015). Image processing techniques for object tracking in video surveillance- A survey, In Proc. 2015 International Conference on Pervasive Computing (ICPC), Pune, India.
10. Agren, S. () 2015. Object tracking methods and their areas of application: A meta-analysis, Master Thesis in Computing Science.
11. Athanesious, J. and Suresh, P. (2012). Systematic survey on object tracking methods in video. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 242–247.
12. Prasad, S. and Sinha, S. (2011). Real-time Object Detection and Tracking in an Unknown Environment, In Proc. of 2011 World Congress on Information and Communication Technologies, 11-14 Dec. 2011, pp. 1056-1061.
13. Ahn, H. and Rhee, S.-B. (2015). Research of Object Recognition and Tracking Based on Feature Matching, Computer Science and its Applications, 2015, pp. 1071-1076.
14. Wang, Q., Zhang, Li., Bertinetto, L., Hu, W. and Philip H.S. Fast Online Object Tracking and Segmentation: A Unifying Approach, arXiv: 1812.05050 [cs.CV].
15. Zhu, Li. and Hu, T. (2013). Research of CamShift Algorithm to Track Motion Objects, TELKOMNIKA, 11(8), August 2013, pp. 4372-4378.
16. Comaniciu, D. Ramesh, V. and Meer, P. (2000). Real-time Tracking of Non-rigid Objects Using Mean Shift, In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2, pp. 142-149.
17. Kingsbury, N. G. (2001). Complex wavelets for shift invariant analysis and filtering of signals. Journal of Applied and Computational Harmonic Analysis, 10 (3), 234–253.
18. Haritaoglu, I., Harwood, D. and Davis, L.S. (2000). W4: real-time surveillance of people and their activities. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(8), 809-830.
19. He, Q. and Chu, C.H. (2011). Layer-based object detection and tracking with graph matching, In Proc. Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VIII; 80200N.
20. Talmi, I. and Roey, Z.-M. (2016). Template Matching with Deformable Diversity Similarity.
21. Arulampalam, M., Maskell, S., Gordon, N. and Clapp, T. (2010). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 50 (2), 174-180.
22. Maggio, E. and Cavallaro, A. (2010). Video Tracking: Theory and Practice. 1. Video Tracking provides a comprehensive treatment of the fundamental aspects of algorithm and application development for the task of estimating, over time.
23. Chandrasekaran, K. (2010). Parametric & Non-parametric Background Subtraction Model with Object Tracking for VENUS. 1. Background subtraction is the process by which we segment moving regions in image sequences.
24. http://visagetechnologies.com/silhouette.
25. Yilmaz, A., Shafique, K. and Shah, M.(2002). Estimation of rigid and nonrigid motion using anatomical face model, In Proc. Int. Conf. of Pattern Recognition, 1, pp.377-380.
Published
2022-07-20
How to Cite
Fareed, S., Bhatia, K., Bhadola, S., & Sharma, R. (2022). An Overview of Object Tracing Techniques in Videos and Images. Central Asian Journal of Theoretical and Applied Science, 3(7), 146-156. Retrieved from https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/812
Section
Articles
