Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Visual Tracking Algorithms - Recent Trends and Challenges: Studying recent trends and challenges in visual tracking algorithms for tracking objects of interest in videos over time

Dr. Michael Abrahamson
Professor of Computer Science, University of Calgary, Canada
Cover

Published 07-07-2022

Keywords

  • Visual tracking,
  • Object tracking

How to Cite

[1]
Dr. Michael Abrahamson, “Visual Tracking Algorithms - Recent Trends and Challenges: Studying recent trends and challenges in visual tracking algorithms for tracking objects of interest in videos over time”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 59–67, Jul. 2022, Accessed: Sep. 19, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/116

Abstract

Visual tracking algorithms play a crucial role in various applications such as surveillance, autonomous driving, and human-computer interaction. This paper presents a comprehensive review of recent trends and challenges in visual tracking algorithms. We discuss the evolution of tracking algorithms from traditional methods to modern deep learning-based approaches. The paper also highlights the key challenges faced by current tracking algorithms, including occlusion, scale variation, and motion blur. Furthermore, we analyze the impact of datasets and evaluation metrics on tracking algorithm performance. Finally, we identify future research directions to improve the robustness and efficiency of visual tracking algorithms.

Downloads

Download data is not yet available.

References

  1. Prabhod, Kummaragunta Joel. "ANALYZING THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES IN IMPROVING PRODUCTION SYSTEMS." Science, Technology and Development 10.7 (2021): 698-707.
  2. Sadhu, Amith Kumar Reddy, and Ashok Kumar Reddy Sadhu. "Fortifying the Frontier: A Critical Examination of Best Practices, Emerging Trends, and Access Management Paradigms in Securing the Expanding Internet of Things (IoT) Network." Journal of Science & Technology 1.1 (2020): 171-195.
  3. Tatineni, Sumanth, and Karthik Allam. "Implementing AI-Enhanced Continuous Testing in DevOps Pipelines: Strategies for Automated Test Generation, Execution, and Analysis." Blockchain Technology and Distributed Systems 2.1 (2022): 46-81.
  4. Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
  5. Perumalsamy, Jegatheeswari, Chandrashekar Althati, and Lavanya Shanmugam. "Advanced AI and Machine Learning Techniques for Predictive Analytics in Annuity Products: Enhancing Risk Assessment and Pricing Accuracy." Journal of Artificial Intelligence Research 2.2 (2022): 51-82.
  6. Devan, Munivel, Lavanya Shanmugam, and Chandrashekar Althati. "Overcoming Data Migration Challenges to Cloud Using AI and Machine Learning: Techniques, Tools, and Best Practices." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 1-39.
  7. Althati, Chandrashekar, Bhavani Krothapalli, and Bhargav Kumar Konidena. "Machine Learning Solutions for Data Migration to Cloud: Addressing Complexity, Security, and Performance." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 38-79.
  8. Keerthika, R., and Ms SS Abinayaa, eds. Algorithms of Intelligence: Exploring the World of Machine Learning. Inkbound Publishers, 2022.
  9. Sadhu, Ashok Kumar Reddy, and Amith Kumar Reddy. "A Comparative Analysis of Lightweight Cryptographic Protocols for Enhanced Communication Security in Resource-Constrained Internet of Things (IoT) Environments." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 121-142.
  10. Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.