Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    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.
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    Exploring Convolutional Neural Networks for Image Classification and Object Detection
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sadhankar, D.S.; Illa, M.; Shetty, P.; Kumar, S.V.; Megha, M.K.; Ambilwade, R.P.
    Convolutional Neural Networks or CNNs are one of the newest powerful tools in various tasks of computer vision such as image classification or object detection providing the highest accuracy. This paper also aims to evaluate the efficiency of the CNNs in these areas using a real-world dataset from Kaggle. We discuss general issues and ways to address it, such as data augmentation, dropout and choose the best/settled value of hyperparameters for improvement of the model. This paper aimed at analyzing the effectiveness of CNN in learning discriminative features from the images and confirm that CNNs are among the most accurate models for image classification. Moreover, this study also provides suggestions for subsequent research work, including improving the CNN architectures, employing transfer learning, and incorporating interpretation methods to continue enhancing the performance of CNNs in computer vision. © 2024 IEEE.