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Archive Issue – Vol.4, Issue.1 (Jan-Mar 2024)

Archive Issue – Vol.4, Issue.1 (January-March 2024)


Augmented Reality: Enhancing Human-Computer Interaction Through Immersive Technology

Arun Pratap Singh

Research Paper | Journal Paper

Vol.4, Issue.1, pp.1-05, Mar-2024

Abstract References Citation Download

Abstract

Augmented Reality (AR) is an emerging technology that overlays digital content onto the real-world environment, thereby enhancing human perception and interaction with their surroundings. Unlike Virtual Reality (VR), which immerses users in a completely virtual environment, AR seamlessly blends the physical and digital worlds. This paper explores the evolution, applications, and future potential of AR by presenting a comprehensive review of existing literature, identifying current challenges, and proposing solutions for improved AR implementations. The study highlights AR's application in healthcare, education, manufacturing, gaming, and retail while identifying major limitations such as hardware constraints, latency, user experience challenges, and data privacy issues. The proposed work emphasizes integrating advanced computer vision techniques, 5G connectivity, and artificial intelligence (AI) to enhance AR's effectiveness. Experimental outcomes suggest that AR significantly improves learning outcomes, surgical precision, and user engagement in retail environments. Finally, this paper outlines the potential scope of AR in creating immersive smart cities, training simulations, and personalized user experiences.

Key-Words / Index Term: Augmented Reality, Human-Computer Interaction, Mixed Reality, AR Applications, Real-Time Processing, Computer Vision, Mobile AR, Wearable Devices, Machine Learning, Virtual Interaction.

References

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      3. F. Zhou, H. B. Duh, and M. Billinghurst, "Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR," in Proc. IEEE/ACM Int. Symp. Mixed and Augmented Reality, 2008, pp. 193–202.
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      7. S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. MIT Press, 2005.
      8. E. Zhu, A. Hadadgar, I. Masiello, and N. Zary, "Augmented reality in healthcare education: An integrative review," PeerJ, vol. 2, p. e469, 2014.
      9. M. B. Ibáñez and C. Delgado-Kloos, "Augmented reality for STEM learning: A systematic review," Computers & Education, vol. 123, pp. 109–123, 2018.
      10. A. Y. C. Nee, S. K. Ong, G. Chryssolouris, and D. Mourtzis, "Augmented reality applications in design and manufacturing," CIRP Annals, vol. 61, no. 2, pp. 657–679, 2012.
      11. W. Narzt, G. Pomberger, A. Ferscha, D. Kolb, R. Müller, and J. Wieghardt, "Augmented reality navigation systems," Universal Access in the Information Society, vol. 4, pp. 177–187, 2006.
      12. A. Javornik, "Augmented reality: Research agenda for studying the impact of its media characteristics on consumer behaviour," Journal of Retailing and Consumer Services, vol. 30, pp. 252–261, 2016.
      13. J. Paavilainen, H. Korhonen, K. Alha, J. Stenros, E. Koskinen, and F. Mayra, "The Pokémon GO experience: A location-based augmented reality mobile game goes mainstream," in Proc. CHI Conf. Human Factors in Computing Systems, 2017, pp. 2493–2498.
      14. C. Dünser, M. Billinghurst, J. Wen, and A. Lehtinen, "Exploring the use of handheld AR for outdoor navigation," Computers & Graphics, vol. 32, no. 2, pp. 210–220, 2008.
      15. K. Kim, M. Billinghurst, G. Bruder, H. B. Duh, and G. F. Welch, "Revisiting trends in augmented reality research: A review of the 2nd decade of ISMAR (2008–2017)," IEEE Trans. Visualization and Computer Graphics, vol. 24, no. 11, pp. 2947–2962, 2018.
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Citation

Arun Pratap Singh, "Augmented Reality: Enhancing Human-Computer Interaction Through Immersive Technology" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.1, pp.1-05, 2024.

A Deep Analysis of Moving Object Detection & Tracking for Various Machine Learning Approaches

Amit Saxena, Sitesh Kumar Sinha, Sanjeev Kumar Gupta

Research Paper | Journal Paper

Vol.4, Issue.1, pp.06-09, Mar-2024

Abstract References Citation Download

Abstract

Object detection is a significant process for performing computer vision related task. It plays an important role in the field of visual object tracking. If it is talking about the real world then it is a challenging task to detect object with high precision rate because of the mazy in appearance. There are various deep learning approaches through which object can be detected precisely but detecting the moving object and tracking it in every frame is bit more challenging. Moving object detection and tracking have wide variety of application such as border surviellence, road activity detection, forest monitoring and many more. There are various deep learning networks such as CNN, R-CNN, SSD, YOLO and many more, these networks are very much capable to detect the object with large traning model. Object tracking is next process after object detection because it is required to target the intial point with target object that has been detected and need to track as per the motion. The intention of this paper is to deep analyze the machine learning approaches which are used to detect the moving object with tracking.

Key-Words / Index Term: Moving Object Detection, Object Tracking, CNN, R-CNN, SSD, YOLO, Pattern Recognition.

References

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Citation

Amit Saxena, Sitesh Kumar Sinha, Sanjeev Kumar Gupta, "A Deep Analysis of Moving Object Detection & Tracking for Various Machine Learning Approaches" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.1, pp.06-09, 2024.

A Review on Visual Object Tracking Methods & Impact for Benchmarks

Amit Saxena, Sitesh Kumar Sinha, Manish Manoria, Sanjeev Kumar Gupta

Research Paper | Journal Paper

Vol.4, Issue.1, pp.10-16, Mar-2024

Abstract References Citation Download

Abstract

Visual Object Tracking is a significantexploration point in Computer vision pattern recognition. The aim of Visual Object Tracking is to automatically acquire the environment of the object in the ensuing video outlines. Visual tracking is now very useful for tracking various moving objects like football in a match and many more for efficiently tracking the target for better decision making. In Artificial Intelligence; visual tracking is more challengeable because of instability of object in the frames. Conventional techniques are not efficient to deal with this kind of challenges. For tracking the object more efficiently; it has been targeted to achieve it through machine learning techniques.This paper intends to review various implemented researches that accepted the challenges for visual tracking and compare to the ground-truth. There are so many researches have been done in this field but somewhere somehow missing the target in certain benchmarks. The most promising technology which has been used by various researchers is Convolutional Neural Network. A network should be trained with all occlusions and acquire better level of precision with minimal overflows.

Key-Words / Index Term: Visual Tracking, Computer Vision, CNN, ANN, OpenCV, Machine Learning,OTB.

References

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Citation

Amit Saxena, Sitesh Kumar Sinha, Manish Manoria, Sanjeev Kumar Gupta, "A Review on Visual Object Tracking Methods & Impact for Benchmarks" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.1, pp.10-16, 2024.

A Review of Visual Trackers & its Applications over the Real World

Amit Saxena, Sitesh Kumar Sinha, Sanjeev Kumar Gupta

Research Paper | Journal Paper

Vol.4, Issue.1, pp.17-20, Mar-2024

Abstract References Citation Download

Abstract

Visual tracking is one of the hot topics and is widely emerging research in the field of computer vision applications. Over the years, the application of visual tracking has been gradually expanded with the continuous development of technology. Researchers have developed various novel tracking methodologies to improve the performance. Although various approaches has been proposed robust visual tracking remains a great challenge.In this paper various visual tracking methods are surveyed and classified into major classifications and also future trends are identified.

Key-Words / Index Term: Moving Object Detection, Object Tracking, CNN, R-CNN, SSD, YOLO, Pattern Recognition.

References

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Citation

Amit Saxena, Sitesh Kumar Sinha, Sanjeev Kumar Gupta "A Review of Visual Trackers & its Applications over the Real World" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.1, pp.17-20, 2024.