Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
- Title
- Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
- Creator
- Cyriac S.; Harsha B.M.; Woon Kim Y.
- Description
- Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE.
- Source
- International Conference on ICT Convergence, Vol-2022-October, pp. 130-135.
- Date
- 2022-01-01
- Publisher
- IEEE Computer Society
- Subject
- Deep Neural Networks (DNN); Geophone; Human Intrusion Detection; Internet of Things (IoT)
- Coverage
- Cyriac S., Centre for Digital Innovation, Christ (Deemed to Be University), Bengaluru, India; Harsha B.M., Centre for Digital Innovation, Christ (Deemed to Be University), Bengaluru, India; Woon Kim Y., Centre for Digital Innovation, Christ (Deemed to Be University), Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISSN: 21621233; ISBN: 978-166549939-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Cyriac S.; Harsha B.M.; Woon Kim Y., “Seismic Activity-based Human Intrusion Detection using Deep Neural Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20205.