A Hybrid Deep Learning Approach for Violence Detection in Videos Video G r nt lerinde Siddet I eren Sahnelerin Tespiti i in Hibrit bir Derin grenme Yaklasimi

AKBAŞ A., ÜÇGÜN H., Ali A. A.

2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025, Bursa, Türkiye, 10 - 12 Eylül 2025, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/asyu67174.2025.11208351
  • Basıldığı Şehir: Bursa
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: computer vision, DenseNet121, LSTM, smart video surveillance, video processing, violence
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

This study proposes and tests a classification approach that performs spatial feature extraction with a pretrained deep convolutional neural network (CNN) model, DenseNet-121, and temporal pattern analysis with a long-shortterm memory (LSTM) model to classify short-length video clips into 'violent' and 'non-violent' categories. The proposed hybrid method is trained using the Real Life Violent Situations (RLVS) dataset and tested with a 5-fold cross-validation method. The results show that the proposal achieves an average accuracy of 95.22% and provides a generalizable performance with a standard deviation value of 0.64%.