Glass break detection system using deep auto encoders with fuzzy rules induction algorithm
Main uses of glass windows in commercial and residential buildings are prevalent. While a glass-based material has its advantages, it also poses security risks. Therefore, glass break detectors play an important role in security protection for offices and residential buildings. Conventional vibr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
Science Gate
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/69673/ http://irep.iium.edu.my/69673/ http://irep.iium.edu.my/69673/ http://irep.iium.edu.my/69673/1/69673_Glass%20break%20detection%20system%20using%20deep.pdf http://irep.iium.edu.my/69673/2/69673_Glass%20break%20detection%20system%20using%20deep_WOS.pdf |
| Summary: | Main uses of glass windows in commercial and residential buildings are
prevalent. While a glass-based material has its advantages, it also poses
security risks. Therefore, glass break detectors play an important role in
security protection for offices and residential buildings. Conventional
vibration-based and acoustic-based glass break detectors are designed to
detect predetermined temporal and frequency feature thresholds of glass
breakage sound signals. This leads to the inability to differentiate glass break
from environmental sounds (such as the sound of striking objects, heavy
sounds and shouted sounds) that are similar in their amplitude threshold
and frequency pattern. Machine learning based acoustic audio classification
has been popular in security surveillance applications. Researchers are
interested in this research area, and different approaches have been
proposed for anomaly event detection (such as gunshots, glass breakage
sounds, etc.). This paper proposes a new design of a glass break detection
algorithm based on Fuzzy Deep Auto-encoder Neural Network. The
algorithm reduces false alarms and improves detection accuracy.
Experimental results indicate that proposed fuzzy deep auto-encoder
network system attained 95.5% correct detection for the proposed audio
dataset. |
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