Detecting Potential Violent Behavior Using Deep Learning

Dalton Chukwuezugo Owoh

Diplomová práce

Abstract:

In this master's thesis, four deep learning models - DenseNet-121, Inception-v3, ResNet50, and VGG-16 were implemented to detect potential violent behavior by applying transfer learning principles. In the theoretical part, a comprehensive review of literature in the field of human violence detection was conducted to identify prevalent strengths and gaps in existing research work. The results of the experiments conducted in this work showed the best performance with accuracy values of 98%. This work recommends, among other key findings, that future research be geared towards exploring the generalization of results from this experiment across larger datasets with adaptations to a broader domain. The future work will also benefit greatly from further hyperparameter tuning of models with more configurations.

Abstract:

In this master's thesis, four deep learning models - DenseNet-121, Inception-v3, ResNet50, and VGG-16 were implemented to detect potential violent behavior by applying transfer learning principles. In the theoretical part, a comprehensive review of literature in the field of human violence detection was conducted to identify prevalent strengths and gaps in existing research work. The results of the experiments conducted in this work showed the best performance with accuracy values of 98%. This work recommends, among other key findings, that future research be geared towards exploring the generalization of results from this experiment across larger datasets with adaptations to a broader domain. The future work will also benefit greatly from further hyperparameter tuning of models with more configurations.

Keywords

Violence detection, AI, Hyperparameter tuning, Pattern recognition, Convolutional Neural Networks

Jazyk práce: angličtina

Datum vytvoření / odevzdání či podání práce: 13. 5. 2024

Obhajoba závěrečné práce

Univerzita Tomáše Bati ve Zlíně

Fakulta aplikované informatiky

Magisterský studijní program / obor:
Information Technologies / Software Engineering