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Surveillance Video-Based Object Detection by Feature Extraction and Classification Using Deep Learning Architecture

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Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

Abstract

As of late, deep learning has accomplished top exhibitions in object recognition undertakings. Be that as it may, continuously, frameworks having memory or processing restrictions extremely wide and profound organizations with various boundaries comprise a significant impediment. Profound gaining-based object location arrangements arose out of PC vision has spellbound undivided focus as of late. This examination proposes novel method in observation video-based object location by highlight extraction with characterization utilizing profound learning. Here the info information has been gathered as observation video and handled for commotion expulsion, smoothening, standardization. Then, at that point, the handled video has been separated and ordered utilizing concealed convolution fluffy perception brain organizations. The exploratory examination has been completed as far as exactness, accuracy, review, F-1 score, RMSE, NSE. Proposed method attained accuracy of 91%, accuracy of 84%, review of 86%, F-1 score of 76%, RMSE of 61%, and NSE of 48%.

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Author information

Authors and Affiliations

  1. Department of Economics and Management, Kazan Federal University, Elabuga Institute of KFU, Elabuga, Russia, 423604

    Elvir Akhmetshin

  2. Department of Construction, Urgench State University, Urganch, Uzbekistan, 220100

    Sevara Sultanova

  3. Department of Electronics and Instrumentation Engineering, V.R. Siddhartha Engineering College, Vijayawada, 520007, India

    C. S. S. Anupama

  4. Department of Computer Science and Engineering, GITAM School of Technology, Vishakhapatnam Campus, GITAM (Deemed to Be a University), Visakhapatnam, India

    Kollati Vijaya Kumar

  5. Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, 530049, India

    E. Laxmi Lydia

Authors
  1. Elvir Akhmetshin
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  2. Sevara Sultanova
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  3. C. S. S. Anupama
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  4. Kollati Vijaya Kumar
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  5. E. Laxmi Lydia
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Corresponding author

Correspondence to E. Laxmi Lydia .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Akhmetshin, E., Sultanova, S., Anupama, C.S.S., Kumar, K.V., Lydia, E.L. (2023). Surveillance Video-Based Object Detection by Feature Extraction and Classification Using Deep Learning Architecture. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_32

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