Conference Papers

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

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Author: search.filters.author.Reddy Guddeti, R.M.Subject: search.filters.subject.Deep Learning

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    Detecting Semantic Similarity of Documents Using Natural Language Processing
    (Elsevier B.V., 2021) Agarwala, S.; Anagawadi, A.; Reddy Guddeti, R.M.
    The similarity of documents in natural languages can be judged based on how similar the embeddings corresponding to their textual content are. Embeddings capture the lexical and semantic information of texts, and they can be obtained through bag-of-words approaches using the embeddings of constituent words or through pre-trained encoders. This paper examines various existing approaches to obtain embeddings from texts, which is then used to detect similarity between them. A novel model which builds upon the Universal Sentence Encoder is also developed to do the same. The explored models are tested on the SICK-dataset, and the correlation between the ground truth values given in the dataset and the predicted similarity is computed using the Pearson, Spearman and Kendall's Tau correlation metrics. Experimental results demonstrate that the novel model outperforms the existing approaches. Finally, an application is developed using the novel model to detect semantic similarity between a set of documents. © 2021 Elsevier B.V.. All rights reserved.
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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.