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Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2025 / Cilt: 15 Sayı: 4

DenseNet121 and Transfer Learning Based Video Analysis Method for Fight Detection in Streaming Images

Akan Görüntülerde Kavga tespiti için DenseNet121 ve Transfer Öğrenme Tabanlı Video Analiz Yöntemi

Elif Feyza Sari(FIRAT ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, ADLİ BİLİŞİM MÜHENDİSLİĞİ BÖLÜMÜ)0009-0003-4806-9242
Nafiye Nur Apaydın(FIRAT ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, ADLİ BİLİŞİM MÜHENDİSLİĞİ BÖLÜMÜ)0009-0006-3438-7401
Orhan Yaman(FIRAT ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, ADLİ BİLİŞİM MÜHENDİSLİĞİ BÖLÜMÜ)0000-0001-9623-2284
Mehmet Karaköse(FIRAT ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-3276-3788
Pages
1165–1177
Publication Date
2025-12-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These high success rates indicate that the proposed method can be effectively used for fight detection.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Early detection of sudden fight incidents and prompt, effective intervention are crucial for ensuring public safety and preventing potential adverse situations.
📊
Key Findings & Data:In this study, a deep learning-based method is proposed to accurately classify fight and non-fight situations.
💡
Conclusion & Impact:These high success rates indicate that the proposed method can be effectively used for fight detection.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Peliculas veri seti · Hockey veri seti · Anahtar kare çıkarımı

Abstract

Early detection of sudden fight incidents and prompt, effective intervention are crucial for ensuring public safety and preventing potential adverse situations. With the advancements in modern technology, video-based automatic fight detection systems enable security forces and authorities to take proactive measures by providing timely alerts. In this study, a deep learning-based method is proposed to accurately classify fight and non-fight situations. Specifically, video-based fight detection is performed using transfer learning with the DenseNet121 model, which has demonstrated successful results in image processing tasks. In the experimental studies, the widely used Peliculas and Hockey datasets from the literature were evaluated. The proposed method achieved 100% accuracy on the Peliculas dataset and 99.32% accuracy on the Hockey dataset. These high success rates indicate that the proposed method can be effectively used for fight detection.

Özet

Ani gelişen kavga olaylarının erken tespiti ve bu olaylara hızlı ve etkili bir şekilde müdahale edilmesi, kamu güvenliğinin sağlanması ve olası olumsuz durumların önlenmesi açısından büyük bir öneme sahiptir. Günümüz teknolojik imkanları doğrultusunda, video tabanlı otomatik kavga tespit sistemleri, güvenlik güçlerine ve yetkililere zamanında uyarılar sağlayarak proaktif önlemler alınmasını mümkün kılmaktadır. Bu çalışmada, kavga (fight) ve kavga içermeyen (noFight) durumların doğru bir şekilde sınıflandırılabilmesi amacıyla derin öğrenme tabanlı bir yöntem önerilmektedir. Özellikle, görüntü işleme alanında başarılı sonuçlar elde eden DenseNet121 modeli kullanılarak transfer öğrenme yöntemiyle video tabanlı kavga tespiti gerçekleştirilmiştir. Deneysel çalışmalarda, literatürde yaygın olarak kullanılan Peliculas ve Hockey veri setleri değerlendirilmiş olup, önerilen yöntem ile Peliculas veri seti üzerinde %100 doğruluk (Accuracy), Hockey veri seti üzerinde ise %99.32 doğruluk elde edilmiştir. Elde edilen yüksek başarı oranları, önerilen yöntemin kavga tespitinde etkin bir şekilde kullanılabileceğini göstermektedir.

🔖

Cite This Article

Sari, E. F., Apaydın, N. N., Yaman, O., Karaköse, M. (2025). DenseNet121 and Transfer Learning Based Video Analysis Method for Fight Detection in Streaming Images. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 15 Sayı: 4, 1165–1177. https://doi.org/10.21597/jist.1649399

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