Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A Key-frame Extraction for Object Detection and Human Action Recognition in Soccer Game Videos(Institute of Electrical and Electronics Engineers Inc., 2023) Chopra, H.; Mundody, S.; Reddy Guddeti, R.M.In professional team games, sports analysts frequently analyze to learn tactical and strategic insights into the actions of players in these team games. The foundation of current analytic procedures is the examination of team footage. We provide a visual analytical framework that seamlessly combines abstract visualizations with team sports video recordings. It offers an exciting opportunity because several complicated, real-time occurrences are examined towards making strategic decisions. Visual object detection is a well-known and active research area. Any object, its speed, and its appearance have their level of detection difficulty in the face of numerous obstacles. Human Action Recognition (HAR) is required to carry out advanced operations in team games as there is an increase in demand for video analysis of sporting events. To strategically improve the team's performance, the team coach may, for instance, use an automatic monitoring system to monitor the player's movement and locations throughout a soccer match as well as the location of the football. This paper proposes a YOLOv7 model that uses the key-frame selection technique to analyze players' actions during a soccer game. In addition to detecting the football, player, and referee, the deep learning model can recognize six of the human actions in the soccer game. The experimental results show that using the key-frame selection technique for human action recognition, the total execution time can be reduced by approximately 68% to 70%. © 2023 IEEE.Item An Effective Early Detection and Prediction System for Gas Leakage in Smart Environments(Institute of Electrical and Electronics Engineers Inc., 2023) Ekka, N.; Mundody, S.; Reddy Guddeti, R.M.Gas leakages can be catastrophic, resulting in human injuries and financial losses. If the gas leaks can be detected and predicted before time, it can significantly help prevent any hazards. This paper proposes to develop a gas leakage detection system using reliable techniques to avoid such situations. The key objective of this paper is to develop a detection and prediction method to identify gas leak situations and predict the amount of gas released and its concentration by the time of release. A sensor-based approach and the Internet of Things (IoT) are employed to find gas leaks in enclosed spaces. For tasks involving detection and prediction, deep learning methods like Long Short-Term Memory (LSTM) networks are used. For evaluation purposes, this paper also compares the suggested strategy with other state-of-art techniques. Additionally, a monitoring and alert system is developed to notify users about gas leakage and hazards. © 2023 IEEE.Item Leveraging SIR and Barabási-Albert Models for Epidemic Modelling(IEEE Computer Society, 2024) Bhat, S.; Ragha Sai, V.; Mundody, S.; Guddeti, R.M.R.The Susceptible, Infected, and Recovered (SIR) model predicts the number of living beings in a population who are infected and recovering from a disease. This article addresses the critical challenge of modelling and simulating the spread of contagious diseases in a population. Drawing inspiration from global events like the COVID-19 pandemic, our proposed simulation aims to comprehensively understand the epidemic dynamics and thus enhances the public awareness for effective decision-making. The proposed simulation integrates the computational models and simulation techniques, including the logistic functions, agent-based models, SIR models, and network-based spread models. © 2024 FRUCT.Item A Hybrid Fog-Cloud Framework for Smart Refrigerator Inventory Management(Institute of Electrical and Electronics Engineers Inc., 2024) Mundody, S.; Guddeti, R.M.R.Technological advances have led to the proliferation of smart home applications, which rely on sensors. The sensors used in smart home applications generate vast amounts of data. Typically, this data gets transmitted to distant cloud data centers for analysis and storage, significantly impacting device performance and user experience. However, introducing the fog nodes at intermediate layers can alleviate the aforesaid issues by conducting most data processing operations locally and offloading only the resource-intensive tasks to the cloud. This paper proposes to showcase the efficacy of the hybrid fog-cloud-based framework for the smart refrigerator inventory management. Using a simulation tool, we evaluated the framework's efficiency against a conventional cloud-based system. The results demonstrate the clear advantages of our proposed framework with respect to network usage, power consumption and delay. © 2024 IEEE.Item An Efficient AI and IoT Enabled System for Human Activity Monitoring and Fall Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Verma, N.; Mundody, S.; Guddeti, R.M.R.Falls present a significant health risk, particularly among the elderly, necessitating reliable wearable fall detection systems. This paper introduces an advanced AI-powered system that integrates Generative Adversarial Networks (GANs) for synthetic data augmentation and Convolutional Neural Networks (CNNs) for robust fall detection and daily activity recognition. The primary challenge in developing effective fall detection systems lies in the scarcity and diversity of real-world fall data. This paper addresses this challenge innovatively by employing a GAN trained on datasets of authentic fall events to generate synthetic data. This augmentation strategy significantly expands the training dataset, enhancing the model's capacity to generalize across various fall scenarios and daily activities. The system leverages a specialized 1D CNN architecture designed for processing accelerometer and gyroscope readings obtained from wearable devices, enabling precise feature extraction to distinguish subtle differences between falls and routine movements. The evaluation results demonstrate a notable advancement by achieving a superior accuracy of 99 % for fall detection while minimizing false positives. The developed CNN model can also classify 15 kinds of falls and 19 types of daily life activities. © 2024 IEEE.