MULTI-SENSOR FUSION TECHNOLOGY FOR CREATING IMMERSIVE OUTDOOR VISUAL ART EXPERIENCES

Arpita A. Prajapati; Ponmurugan Panneerselvam; Bipin Sule; Sahaya Anselin Nisha A; Debanjan Ghosh; Mridula Gupta; Asha Rani G

doi:10.29121/shodhkosh.v7.i4s.2026.7480

Authors

Ms. Arpita A. Prajapati Lecturer, Faculty of Engineering, Gokul Global University, Sidhpur, Gujarat, India
Ponmurugan Panneerselvam Professor, Department of Research, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu 600080, India
Bipin Sule Senior Professor, Department of DESH, Vishwakarma Institute of Technology, Pune, Maharashtra 411037, India
Dr. Sahaya Anselin Nisha A Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
Mr. Debanjan Ghosh Assistant Professor, Department of Computer Science and IT, Arka Jain University, Jamshedpur, Jharkhand, India
Mridula Gupta Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
Asha Rani G Assistant Professor, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu 600080, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7480

Keywords:

Multi-Sensor Fusion, Immersive Art Systems, Outdoor Interactive Installations, Multimodal Data Processing, Creative Computing

Abstract [English]

Multi-sensor fusion technology has become a groundbreaking methodology of developing immersive outdoor visual art experiences through the process of combining heterogeneous data into adaptive systems of art. This paper introduces a general concept of a multi-sensor fusion-based art system, which uses visual (RGB-D cameras), motion (IMU), spatial (LiDAR), environmental (temperature, humidity, light), and biometric sensors to support dynamic, context-responsive art-related interactions in the outdoor setting. The suggested methodology makes use of multi-modal feature extraction as a way of visual patterns, motion dynamics, and environmental changes and then applies a hybrid fusion algorithm combining deep learning architectures and statistical models to provide a superior data synthesis and responsiveness to real-time requirements. A time-dependent collection unit assures time synchronization of sensor streams, improving the reliability of the system in different application tasks in the outdoor environment. The results of the experimental assessment show that these systems are much more accurate, responsive and user-engaging in comparison to single sensor and baseline systems. The findings point to improved spatial awareness and adaptive content creation, as well as fidelity of interaction, as a part of an increasingly immersive and individually-centered artistic experience.

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