ct 18: e5

Research Article

An Enhanced ANN-HMM based classification of video recordings with the aid of audio-visual feature extraction

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  • @ARTICLE{10.4108/eai.31-3-2021.169172,
        author={Pooja Mehta and Sahil Kaswan and Jaspreet Kaur},
        title={An Enhanced ANN-HMM based classification of video recordings with the aid of audio-visual feature extraction},
        journal={EAI Endorsed Transactions on Creative Technologies: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={CT},
        year={2021},
        month={3},
        keywords={Hidden Markov model, Artificial neural network, Zero Forcing Equaliser, Mel- frequency cepstral coefficient, Neural Network},
        doi={10.4108/eai.31-3-2021.169172}
    }
    
  • Pooja Mehta
    Sahil Kaswan
    Jaspreet Kaur
    Year: 2021
    An Enhanced ANN-HMM based classification of video recordings with the aid of audio-visual feature extraction
    CT
    EAI
    DOI: 10.4108/eai.31-3-2021.169172
Pooja Mehta1,*, Sahil Kaswan1, Jaspreet Kaur1
  • 1: JCDM College of Engineering, Sirsa Haryana 125055, India
*Contact email: poojamehta0193@gmail.com

Abstract

INTRODUCTION: As an essential part of life, the use of the Internet has increased exponentially. This rising Internet bandwidth speed has made video data transmission a more popular and modern form of information exchange. For classification of video date files there is a requirement of human efforts. Also for reducing the rate of clutter in video data on Internet, a suitable automatic video classification method is required.

OBJECTIVES: In this work, we tried to find a successful model for video classification.

METHODS: To make a successful model we use different schemes of visual and audio data analysis. On the other hand we choose some music, traffic and sports videos for different analysis. The model is based on Hidden Markov model (HMM) and Artificial neural network (ANN) classifiers. In order to gather the final results, we developed an “enhanced ANN-HMM based” model.

RESULTS: Our approach attained an average of 90% success rate among all three classification classes.

CONCLUSION: In aim of this work is to categorize and caption the videos automatically. Here we proposed an enhanced HMM-ANN based classification of video recordings with the aid of audio visual feature extraction.