We have previously proposed an indoor monitoring and security system with an array sensor. The array sensor has some advantages, such as low privacy concern, easy installation with low cost, and wide detection range. Our study is different from the previously proposed classification method for array sensor, which uses a threshold to classify only two states for intrusion detection: nothing and something happening. This paper describes a novel state classification method based on array signal processing with a machine learning algorithm. The proposed method uses eigenvector and eigenvalue spanning the signal subspace as features, obtained from the array sensor, and assisted by multiclass support vector machines (SVMs) to classify various states of a human being or an object. The experimental results show that our proposed method can provide high classification accuracy and robustness, which is very useful for monitoring and surveillance applications.
ASJC Scopus subject areas
- コンピュータ ネットワークおよび通信