International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024
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p-ISSN: 2395-0072
CONTINUOUS MONITORING AND TRACING OF OBJECTS IN REAL-TIME FOR DETECTION: A REVIEW 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 - Continuous surveillance and monitoring of
objects in real-time are now essential in various sectors such as surveillance, transportation, and manufacturing. Despite its importance, detecting objects in dynamic environments poses significant challenges, including obstructions, changing lighting conditions, and complex backgrounds. This review delves into the progression of computer vision algorithms, particularly highlighting the shift in approach brought by deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which have greatly enhanced the accuracy of object detection. Additionally, it explores the incorporation of sensor technologies such as LiDAR, radar, and infrared sensors, which enhance detection performance, especially in difficult environmental conditions. Data fusion methods are also examined for their ability to combine data from different sensors, thereby improving the precision of object tracking. Moreover, the article emphasizes the importance of real-time processing and edge computing, facilitating the implementation of object detection systems in environments with limited resources and strict latency requirements. It also discusses the growing applications in fields like autonomous vehicles, smart cities, and Internet of Things (IoT) devices that are driving the need for effective real-time object detection solutions. Lastly, the review outlines current unresolved research challenges and suggests future research directions to advance the field, focusing on areas such as improving detection accuracy in complex scenarios, strengthening defense against adversarial attacks, and developing energyefficient algorithms for edge deployment. Key Words: Object detection, Real-time monitoring, Deep
learning, Sensor fusion, Convolutional neural networks (CNNs), Recurrent neural networks (RNNs).
1.HISTORY The continuous surveillance and tracking of objects in realtime for detection have experienced a remarkable evolution due to technological advancements and industry demands. This practice can be traced back to the development of radar systems during World War II, which established the foundation for continuous object tracking using electronic sensors. In time, various industries began to adopt similar principles for tracking goods within supply chains, with the assistance of innovations such as barcode technology. The © 2024, IRJET
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Impact Factor value: 8.226
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introduction of RFID technology further transformed tracking capabilities, allowing for real-time monitoring without the need for line-of-sight scanning. The rise of the Internet of Things (IoT) brought about a new era of real-time monitoring across different sectors, as interconnected devices equipped with sensors became prevalent. Subsequently, machine learning and artificial intelligence (AI) have bolstered these systems, facilitating predictive analytics and anomaly detection. The integration with cloud computing has also broadened scalability and accessibility. Presently, continuous monitoring and tracking systems are utilized in various industries to ensure efficiency, safety, and security. Looking ahead, ongoing innovations in sensor technology, data analytics, and connectivity are set to drive further advancements to meet the increasing demand for real-time visibility and control.
2.REAL TIME DETECTION Instantaneous identification, a crucial capability spanning various industries, allows for immediate recognition and response to unfolding circumstances or alterations without any notable delay. It forms the foundation in security, healthcare, finance, and manufacturing, where swift identification of irregularities or trends is essential for prompt decision-making and implementation. Security applications utilize instantaneous identification to analyze video feeds, sensor data, or network traffic, prompting alarms or notifications in the presence of potential dangers or questionable activities. In the healthcare sector, continuous monitoring of vital signs and physiological parameters enables early detection of medical crises, leading to timely interventions and enhanced patient outcomes. Financial institutions utilize instantaneous identification systems to analyze transactions and market data for the prevention of fraud and to capitalize on market opportunities. Similarly, in manufacturing, instantaneous identification enhances operational efficiency by monitoring equipment performance and predicting maintenance requirements, thus reducing downtime. With advancements in sensor technology, data analytics, and artificial intelligence, instantaneous identification systems have become more proficient at swiftly and accurately recognizing events or patterns of interest. Integration with cloud and edge computing further enhances scalability and adaptability, enabling these systems to effectively manage ISO 9001:2008 Certified Journal
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