Abstract
With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.
Highlights
The development of modern science and technology has made people’s living environments gradually become intelligent and networked
We propose a novel way that extracts the instantaneous amplitude and instantaneous frequency of moving human target as eigenvalues by using empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT) algorithms on the basis of an established distributed wireless pyroelectric infrared sensor network
When one or more human targets move in the detection area, the host computer can process the signals received from the wireless gateway module and the tracking results are displayed on the color screen of a personal digital assistant (PDA)
Summary
The development of modern science and technology has made people’s living environments gradually become intelligent and networked. All sorts of environmental monitoring systems [1,2], security systems and intelligent auxiliary systems [3] are increasingly being widely used in the home, office, factory or harsh working conditions such as mines In these applications, the Sensors 2014, 14 accurate identification of moving human targets is crucial for physical protection and emergency relief, etc. Traditional video systems [6,7] have been applied in many recognition scenarios, they mainly identify a person using facial features which are greatly affected by many external factors such as lighting, angle or clothes. They usually have high computational overhead and require huge data throughput. In a distant or crowded scene, it is a very complicated problem to identify a human target using some behavioral biometric features, whose main reason is closely related to the perception of human object form and feature selection
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