Motion Capture Autonomous Drone

• Published in: Journal of information systems engineering & management Vol. 10; no. 29s; pp. 929 - 933
• Main Author: Dr. Yogesh Suryawanshi
• Format: Journal Article
• Language: English
• Published: 29.03.2025
• ISSN: 2468-4376; 2468-4376
• DOI: 10.52783/jisem.v10i29s.4605

Introduction: Motion capture technology is vital in industries like sports biomechanics, cinema, Robotics traditional system rely on fixed cameras limited flexibility in dynamic outdoor environments autonomous rule equipped with AI and computer vision of announced mobility, real time tracking of moving subject across diversity. Drones enable resize performing analysis in sports and dynamic film without need for complex setup all the challenges like environmental conditions and hardware limitation remains research in sensor fusion and optimization is improving reliability Drone represent a major advancement in motion capture, unlocking new possibilities across multiple field as technology evolves motion capture will become more efficient and accessible.Objectives: Recent advancement in motion capture technology have addressed limitation of traditional system, which relay on stationery cameras and makers in control environment. These traditional system are expensive in mobile and labour intensive Drone based motion capture offers greater mobility, flexibility and real time tracking in divers environment making it suitable for application of film, sports and biomechanics. Researchers are exploring rules stabilization techniques real time tracking algorithm and multi-drone coordination for large scale projects. Drones represent the next evolution unable in dynamic tracking in complex & natural settings.Methods: The study proposes fully automated Drone- based motion capture system leveraging advanced computer vision real time processing and stabilization for accurate tracking in dynamic environments. The system architecture integrates higher revolution cameras, depth sensor, IMUs and on board processing unit unable real time motion tracking. Computer vision technique including deep learning models like YOLO and SSD, facilitate high speed detection and classification, while optical flow methods and features tracking enhance accuracy. Sensor fusion integrates data from various sources to improve tracking precision and Kalman filtering reduces noise. AI-driven flight control and autonomous navigation algorithm ensure stable positioning and obstacle avoidance. Multi-drone coordination allows for large scale motion capture while performance strategies like energy-efficient and adaptive frame rates improve system efficiency.Results: The innovative fusion of autonomous drones and motion capture technology provide flexible, precise and low-cost alternative to traditional motion capture system. Bye integrating computer vision, real-time sensor fusion and AI driven tracking algorithm, it overcome the limitation of fixed camera setup, improving tracking in dynamic and unstructured environments. The system ability to track multiple subjects without makers reduce operational cost and complexity. AI driven object recognisation models achieve high accuracy, even in challenging lightning and occulasion condition. Future improvements will focus on multi-drone cordination, avoidance and incorporating thermal or infrared imaging to address poor lighting condition. Collaborative Drone efforts could good expand the systems’s application in technical field.Conclusions: The integration of autonomous Drone with motion capture technology offers promising advancement in multiple domains, with potential for enhanced efficiency and accuracy. Future research will focus on improving real time motion tracking processing through energy efficient AI chips unable in faster data fusion and on board processing. Edge AI models will allow drones to make autonomous decisions in dynamic environment, without the need for external servers. Multi-drone coordination will unable large scale motion capture applicable to sports, film production and biomechanics. Enhancing object detection in challenging environment like low light and weather, conditions along with thermal imaging and LIDAR will improve robustness. The systems integration with AR and VR technology could transform gaming, training and immersive stimulations. Future developments will focus on energy-efficient flight algorithms, longer battery life and application in industries like healthcare, Robotics, defence and disaster management. Additionally improved safety mechanisms including collision avoidance, will ensure safe operation in complex environments.