| Title: |
SynthSecureNet: An Improved Deep Learning Architecture with Application to Intelligent Violence Detection. |
| Authors: |
Zungu, Ntandoyenkosi1 (AUTHOR) 223048675@student.uj.ac.za, Olukanmi, Peter1 (AUTHOR) polukanmi@uj.ac.za, Bokoro, Pitshou1 (AUTHOR) |
| Source: |
Algorithms. Jan2025, Vol. 18 Issue 1, p39. 19p. |
| Subject Terms: |
*CRIME prevention, *VIDEO surveillance, *VIOLENCE, *DEEP learning |
| Abstract: |
We present a new deep learning architecture, named SynthSecureNet, which hybridizes two popular architectures: MobileNetV2 and ResNetV2. The latter have been shown to be promising in violence detection. The aim of our architecture is to harness the combined strengths of the two known methods for improved accuracy. First, we leverage the pre-trained weights of MobileNetV2 and ResNet50V2 to initialize the network. Next, we fine-tune the network by training it on a dataset of labeled surveillance videos, with a focus on optimizing the fusion process between the two architectures. Experimental results demonstrate a significant improvement in accuracy compared with individual models. MobileNetV2 achieves an accuracy of 90%, while ResNet50V2 achieves a 94% accuracy in violence detection tasks. SynthSecureNet achieves an accuracy of 99.22%, surpassing the performance of individual models. The integration of MobileNetV2 and ResNet50V2 in SynthSecureNet offers a comprehensive solution that addresses the limitations of the existing architectures, paving the way for more effective surveillance and crime prevention strategies. [ABSTRACT FROM AUTHOR] |
|
Copyright of Algorithms is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Academic Search Complete |
|
Full text is not displayed to guests. |
Login for full access.
|
