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Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

Efficient object tracking through machine learning and optimization strategies in computer vision

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* Corresponding author · click or hover a name for details

pp. 327–336Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1258XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1258
Pages:
327–336

Abstract

This research introduces an innovative approach for efficiently monitoring objects in computer vision systems through combining machine learning (ML) algorithms and optimization strategies. Tracking objects is crucial in applications like surveillance, self-driving vehicles, and augmented reality. By harnessing advanced ML methods such as deep learning and integrating optimization strategies including gradient-based techniques, the proposed system aims to enhance accuracy and real-time performance during tracking. The collaborative synergy between ML algorithms and optimization methods empowers the system to dynamically adjust to diverse and challenging scenarios, ensuring resilient tracking across varying environments. Experimental results underscore the efficacy of the proposed methodology, demonstrating superior tracking precision compared to conventional approaches. This study contributes to the progression of computer vision applications, providing a scalable and adaptable solution for real-world challenges in object tracking. 

Keywords

Object detectionDeep learningComputer visionChallengesApplications

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] C.Stauffer and W.Grimson.Adaptive background mixture models for real-time tracking. Proceedings of IEEE Conf.Computer Vision and pattern Recognition,vol. 2, 246-252 (1999).
[2] R.T Collins, et al. A System for video surveillance and monitoring: VSAM final report.CMU-TR-0012, Technical Report, Carnegie Mellon University (2000).
[3] 3. A.J. Lipton, H. Fuijiyoshi, R.S Patil. Moving target classification and tracking from real-time video. Proceedings of the IEEE Workshop on Application of Computer Vision, 8-14 (1998).
[4] Y. Kuno, T. Wantance , Y. shimosakoda , S. Nakagawa. Automated detection of human for visual surveillance system. Proceedings of the International Conference on Pattern Recognition, 865-869 (1996).
[5] R. Culter, L.S Davis. Robust real-time periodic motion detection, analysis, and application. IEEE Trans. Pattern Anal.Mech. Intell. 22 (8), 781-796 (2000).
[6] Local application of optic flow to analysis rigid versus non-rigid motion.
[7] A.Selinger, L. Wixson. Classifying moving objects as rigid or non-rigid without correspondences. Proceedings of the DAPRA Image Understanding Workshop, Vol. 1, 341-358 (1998).
[8] M. Oren, et al. Pedestrian detection using wavelet templates. Proceedings of the IEEE CS Conference on Computer Vision and Pattern Recognition. 193-199 (1997).
[9] C. Stauffer. Automatic hierarchical classification using time-base co-occurrences. Proceedings of the IEEE CS Conference on Computer Vision and pattern Recognition, 333-339 (1999).
[10] Weiming Hu, Tieniu Tan, Liang Wang, and Steve Maybank. A Survey on Visual Surveillance of Object Motion and Behaviors.
[11] J. K. Aggarwal, Q. Cai, W. Liao, and B. Sabata. Nonrigid motion analysis: Articulated and elastic motion. A Journal of Computer Vision and Image Understanding. vol. 70, no. 2, 142–156, May (1998).
[12] Manoranjan Paul, Shah M E Haque and Subrata Chakraborty. Human detection in surveillance videos and its applications - a review. EURASIP Journal on Advances in Signal Processing, 2013:176 (2013).

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