Use of Acoustic and Vibration Sensor Data to Detect Objects in Surveillance Wireless Sensor Networks

Date

2017-05-31

Author

Kucukbay, Selver Ezgi
SERT, MUSTAFA
Yazıcı, Adnan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Nowadays, people are using stealth sensors to detect intruders due to their low power consumption and wide coverage. It is very important to use lightweight sensors for detecting real time events and taking actions accordingly. In this paper, we focus on the design and implementation of wireless surveillance sensor network with acoustic and seismic vibration sensors to detect objects and/or events for area security in real time. To this end, we introduce a new environmental sensing based system for event triggering and action. In our system, we first design an appropriate hardware as a part of multimedia surveillance sensor node and use proper classification technique to classify acoustic and vibration data that are collected by sensors in real-time. According to the type of acoustic data, our proposed system triggers a camera event as an action for detecting intruder (human or vehicle). We use Mel Frequency Cepstral Coefficients (MFCC) feature extraction method for acoustic sounds and Support Vector Machines (SVM) as classification method for both acoustic and vibration data. We have also run some experiments to test the performance of our classification approach. We show that our proposed approach is efficient enough to be used in real life.

Subject Keywords

Wireless sensor networks, Raspberry pi, Acoustic sensor, Vibration sensor, MFCC, SVM, Classification

URI

https://hdl.handle.net/11511/36614

DOI

https://doi.org/10.1109/cscs.2017.35

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

S. E. Kucukbay, M. SERT, and A. Yazıcı, “Use of Acoustic and Vibration Sensor Data to Detect Objects in Surveillance Wireless Sensor Networks,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36614.