ICTACT Journal on Image and Video Processing

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An Improvised Ensemble CNN Algorithm for Detectting Video Stream in MultimediaAn Improvised Ensemble CNN Algorithm for Detectting Video Stream in Multimedia


Affiliations
1 Data Science, Codecraft Technologies, Bangalore, India
2 Department of Computer Science and Engineering, PSV College of Engineering and Technology, India
3 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, India
4 Department of Computer Science and Engineering, Hindusthan Institute of Technology, India
     

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The only criteria that are used to evaluate the various neural network-based object identification models that are currently in use are the inference times and accuracy levels. The issue is that in order to put these new classes and situations to use in smart cities, we need to train on them in real time. We were not successful in locating any research or comparisons that were centered on the length of time necessary to train these models. As a direct consequence of this, the initial reaction times of these object identification models will consistently be quite slow (maybe in days). As a consequence of this, we believe that models that put an emphasis on the speed of training rather than accuracy alone are in significant demand. Users are able to gather photos for use in training in the present by utilizing concept names in online data collection toolkits; however, these images are iconic and do not have bounding boundaries. Under these conditions, the implementation of semi-supervised or unsupervised models in a variety of smart city applications might be able to contribute to an improvement in the precision of data derived from IoMT. In this study, we categorize the video clips into their appropriate classes using an improved ensemble classification model.

Keywords

CNN, Ensemble, Video Stream, IoT.
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  • David Money Harris, and Sarah L. Harris, “Digital Design and Computer Architecture”, Morgan Kaufmann, 2007.

  • J. Bethencourt, D. Song and B. Waters, “New Techniques for Private Stream Searching”, ACM Transactions on Information and System Security, Vol. 12, No. 3, pp. 1-32, 2009.

  • X.V. Nguyen and N.N. Dao, “Intelligent Augmented Video Streaming Services Using Lightweight QR Code Scanner”, Proceedings of IEEE International Conference on Communication, Networks and Satellite, pp. 103-107, 2021.

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  • X. Jin and J. Xu, “Towards General Object-Based Video Forgery Detection via Dual-Stream Networks and Depth Information Embedding”, Multimedia Tools and Applications, Vol. 81, No. 25, pp. 35733-35749, 2022.

  • S.M. Kulkarni, D.S. Bormane and S.L. Nalbalwar, “Coding of Video Sequences using Three Step Search Algorithm”, Proceedings of International Conference on Advance in Computing, Communication and Control, pp. 34-42, 2015.

  • K. Leela Bhavani and R. Trinadh, “Architecture for Adaptive Rood Pattern Search Algorithm for Motion Estimation”, International Journal of Engineering Research and Technology, Vol. 1, No. 8, pp. 1-6, 2012

  • R. Sudhakar and S. Letitia, “Motion Estimation Scheme for Video Coding using Hybrid Discrete Cosine Transform and Modified Unsymmetrical-Cross Multi Hexagon-Grid Search Algorithm”, Middle-East Journal of Scientific Research, Vol. 23, No. 5, pp. 848-855, 2015.

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  • Shih-Hao Wang, Shih-Hsin Tai and Tihao Chiang, “A LowPower and Bandwidth-Efficient Motion Estimation IP Core Design using Binary Search”, IEEE Transactions on Circuits and System for Video Technology, Vol. 19, No. 5, pp. 760-765, 2009.

  • D. Nagothu and A. Aved, “Detecting Compromised Edge Smart Cameras using Lightweight Environmental Fingerprint Consensus”, Proceedings of ACM Conference on Embedded Networked Sensor Systems, pp. 505-510, 2021.

  • B. Zawali, S. Furnell and A. A-Dhaqm, “Realising a Push Button Modality for Video-Based Forensics”, Infrastructures, Vol. 6, No. 4, pp. 54-62, 2021.


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  • An Improvised Ensemble CNN Algorithm for Detectting Video Stream in MultimediaAn Improvised Ensemble CNN Algorithm for Detectting Video Stream in Multimedia

Abstract Views: 181  |  PDF Views: 1

Authors

C. Kiran Kumar
Data Science, Codecraft Technologies, Bangalore, India
S. Chandra Sekaran
Department of Computer Science and Engineering, PSV College of Engineering and Technology, India
R. Gayathri
Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, India
S. Ramasamy
Department of Computer Science and Engineering, Hindusthan Institute of Technology, India

Abstract


The only criteria that are used to evaluate the various neural network-based object identification models that are currently in use are the inference times and accuracy levels. The issue is that in order to put these new classes and situations to use in smart cities, we need to train on them in real time. We were not successful in locating any research or comparisons that were centered on the length of time necessary to train these models. As a direct consequence of this, the initial reaction times of these object identification models will consistently be quite slow (maybe in days). As a consequence of this, we believe that models that put an emphasis on the speed of training rather than accuracy alone are in significant demand. Users are able to gather photos for use in training in the present by utilizing concept names in online data collection toolkits; however, these images are iconic and do not have bounding boundaries. Under these conditions, the implementation of semi-supervised or unsupervised models in a variety of smart city applications might be able to contribute to an improvement in the precision of data derived from IoMT. In this study, we categorize the video clips into their appropriate classes using an improved ensemble classification model.

Keywords


CNN, Ensemble, Video Stream, IoT.

References