International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024
www.irjet.net
p-ISSN: 2395-0072
CONTINUOUS MONITORING AND TRACING OF OBJECTS IN REAL-TIME FOR DETECTION Abhay Kumar Srivastava1, Dipti Ranjan Tiwari2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In today's fast-paced and interconnected world,
the ability to monitor and track objects in real-time is essential for a wide range of applications, such as security, surveillance, inventory management, and autonomous navigation. This research paper introduces a novel approach to real-time object detection and tracking by utilizing advanced computer vision techniques and cutting-edge machine learning algorithms. The system we propose combines continuous monitoring with efficient tracing mechanisms to ensure precise and timely detection of objects in various settings. Some key aspects of our approach include handling occlusions effectively, adapting in real-time to changing conditions, and the ability to scale up for large deployments. Through extensive experiments and evaluations, we have demonstrated the effectiveness and reliability of our system, highlighting its potential to improve operational efficiencies and enhance situational awareness across different application domains. Our findings show significant enhancements in detection accuracy and processing speed when compared to existing methods, setting a new standard for real-time object tracking systems. This innovative approach holds promise for revolutionizing how objects are monitored and tracked in real-time for a variety of practical purposes. Key Words: real-time monitoring, object detection, object tracking, computer vision, machine learning, surveillance, autonomous navigation, occlusion handling, scalability, situational awareness, operational efficiency.
1.INTRODUCTION The capability to monitor and track objects in real-time has far-reaching implications in various fields. In the realm of security, real-time monitoring enhances surveillance techniques, allowing for improved safety measures. In the transportation sector, this technology plays a crucial role in enhancing traffic management and ensuring the safety of commuters. Additionally, in the healthcare industry, realtime tracking aids in patient monitoring and asset management, leading to more efficient and effective healthcare delivery. The advancements in computer vision, machine learning, and sensor technologies have been instrumental in driving progress in real-time object detection and tracking. These technologies have paved the way for innovative solutions that have revolutionized the way objects are monitored and traced in real-time. This © 2024, IRJET
|
Impact Factor value: 8.226
|
paper aims to explore the intricate mechanisms behind realtime object detection and tracking, providing an in-depth analysis of the current methodologies, technological advancements, and practical applications of this cutting-edge technology. Through this exploration, we hope to shed light on the potential of real-time object detection and tracking in shaping the future of various industries.
2.DATA ASSOCIATION THEORY FOR OBJECT TRACING The theory of data association is a fundamental concept that holds a crucial role in the field of object tracking. By enabling the accurate matching of observations with existing object trajectories over time, this theory addresses the challenges associated with linking sensor measurements in complex and cluttered environments. A vital aspect of data association theory involves developing algorithms that can effectively assign observations to object trajectories while taking into account factors such as measurement error, trajectory dynamics, and potential obstructions. There are various techniques for data association, including nearest neighbor methods, probabilistic approaches such as the Kalman filter, and more sophisticated methods like joint probabilistic data association (JPDA) and multiple hypothesis tracking (MHT). These algorithms are designed to streamline the association process by utilizing available data to establish the most likely connections between measurements and trajectories, thereby enhancing the accuracy and efficiency of object tracking systems.
3. COMPUTER VISION AND MACHINE LEARNING Real-time object detection and tracking rely on computer vision, a discipline that empowers computers to understand and manipulate visual information. This technology has been significantly enhanced by machine learning algorithms, specifically deep learning, which have transformed the field of computer vision. Thanks to these advanced algorithms, complex models have been created that can accurately identify and track objects in real-time. This breakthrough has opened up new possibilities for applications in various industries, from autonomous vehicles to surveillance systems. The combination of computer vision and machine learning continues to drive innovation and improve the accuracy and efficiency of object detection and tracking systems. ISO 9001:2008 Certified Journal
|
Page 1091