Abstract
Violence is a pressing global issue, threatening public safety and demanding
effective monitoring methods. Video surveillance, particularly remote systems,
plays an important role in detecting and responding to violent incidents but faces
challenges in terms of accuracy, speed, and resource demands. Recent advancements
in deep learning have shown promise in enhancing surveillance capabilities,
providing faster and more accurate detection.
In this dissertation, a new deep learning architecture is presented, named
SynthSecureNet, which addresses these limitations by hybridizing two popular
architectures: MobileNetV2 and ResNet50V2. This study aims to harness the
combined strengths of these models to improve violence detection accuracy while
maintaining computational efficiency. SynthSecureNet leverages the pre-trained
weights of MobileNetV2 and ResNet50V2, employing transfer learning to initialize
the network. The architecture is then fine-tune using the Real-Life Violence
Situations Dataset, consisting of 2000 labelled surveillance videos. The model’s
performance is evaluated using metrics including accuracy, precision, recall, and
F1 score, with particular focus on optimizing the fusion process between the two
base architectures. Experimental results demonstrate significant improvements in
violence detection performance. While MobileNetV2 achieves 90% accuracy and
ResNet50V2 reaches 94%, SynthSecureNet attains a remarkable 99.22% accuracy.
This substantial enhancement in performance is consistent across various dataset
sizes, ranging from 400 to 2000 videos.
The SynthSecureNet architecture offers a comprehensive solution that addresses
the limitations of existing single-model approaches. By combining the efficiency of
MobileNetV2 with the depth of ResNet50V2, this model provides a more robust
and accurate tool for violence detection in surveillance footage. These findings pave
the way for more effective surveillance and crime prevention strategies, potentially
improving response times to violent incidents and enhancing public safety.