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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Jurnal Informasi dan Komputer JSAI (Journal Scientific and Applied Informatics) Journal of Applied Data Sciences Jurnal AbdiMas Nusa Mandiri Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS SWAGATI: Journal of Community Service
Sukmawati Anggraeni Putri, Sukmawati Anggraeni
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Mathematics Physics Social Sciences Other

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Journal : Journal of Applied Data Sciences

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HU Variance Moment Optimizes Keyframe Selection Based on Deep Learning for Violence Detection Putri, Sukmawati Anggraeni; Andono, Pulung Nurtantio; Purwanto, Purwanto; Soeleman, Moch Arief
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.648

Abstract

Violence in public spaces poses a serious threat to individuals and society. Manual monitoring and violence detection require much time and human resources, ultimately hindering detection accuracy and speed. Therefore, an automated method is needed to detect violence to ensure fast and efficient action. Along with technological advances, violence detection research has adopted various methods and models, including deep learning, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In this study, the classification process for detecting violence and non-violence uses the VGG19 model, one of the CNN models that has good performance with limited computing. In addition, the Long Short-Term Memory (LSTM) model is the best RNN model for processing temporal data in videos. However, this performance will decrease with noise and irrelevant data in the classification process. Therefore, to optimize deep learning performance, this study in the pre-processing phase selects keyframes in frame extraction using the Hu Variance Moment Technique. This method calculates each frame’s Hu and Variance Moment values and selects keyframes based on high Hu values. Next, we use Adaptive Moment Estimation (Adam) to optimize the gradient of the selected keyframes. This study produces a Hu19LSTM model tested on three datasets: hockey fight, crowd, and AIRTLab. The proposed Hu19LSTM model produces an accuracy of 97% on the Hockey Fight dataset, 97% on the Crowd dataset, and 95% on the AIRTLab dataset. These results indicate that the Hu19LSTM model can increase its accuracy on the hockey fight and Crowd dataset by 97%.
Co-Authors ACHMAD RIFAI Dewi Anggraini Dewi Larasati, Dewi Dinar Ismunandar Dwiantoro, Tino Gata, Windu Imam Nawawi, Imam Kartika Handayani Kristiana, Titin Laela Kurniawati, Laela Lilyani Asri Utami, Lilyani Asri Marsuyitno, Marsuyitno Melati Melati Moch Arief Soeleman, Moch Arief Oktaviana , Shinta Oktaviana R, Shinta Pulung Nurtantio Andono Purwanto Purwanto Setiaji Setiaji, Setiaji Utami , Lilyani Asri