Enhanced Multi-Model Approach for Motion and Violence Detection using Deep Learning Methods Using Open World Video Game Dataset
- Title
- Enhanced Multi-Model Approach for Motion and Violence Detection using Deep Learning Methods Using Open World Video Game Dataset
- Creator
- Agarwal V.; Singh M.; Prathap B.R.
- Description
- For today's environment, it is extremely important to understand hostility and motion in a variety of contexts, particularly where accidents are concerned. There's also a high safety risk in public places if there is no proper identification of suspicious activities that occur fast and cannot be accurately observed through traditional surveillance systems that rely on constant human monitoring. Although deep learning algorithms have proven useful for detecting anomalies such as fraud recently, there has been little research on real-time crime detection because of issues related privacy when using live data sets. To tackle the key problem of motion and violence detection with current deep learning methods, this work exploits the Open World Game Dataset which provides realistic activities. The reliance on only one technique undermined the previous models' accuracy while this study comes up with various models to raise the detection precision and real-time processing capability. This work applies MobileNet SSD, YOLOv8 (You Only Look Once), and SSD (Single Shot MultiBox Detector) techniques to create a more accurate movement detection system. To identify violent or illegal behavior from videos, 3D convolutional neural networks (3DCNN) will be used alongside attention approaches. A diverse inexpensive training environment that enables simulating. 2024 IEEE.
- Source
- 1st International Conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024 - Proceedings, pp. 355-360.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- 3DCNN; Attention Mechanism; Deep Learning; IoT; MobilenetSSD; YOLOv8
- Coverage
- Agarwal V., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India; Singh M., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India; Prathap B.R., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835036501-6
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Agarwal V.; Singh M.; Prathap B.R., “Enhanced Multi-Model Approach for Motion and Violence Detection using Deep Learning Methods Using Open World Video Game Dataset,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19132.