Active Compressive Sensing via Pyroelectric Infrared Sensor for Human Situation Recognition
本文设计了三种主动热释电红外传感器，能检测静态热源，并基于压缩感知理论采用随机采样，实验表明其比均匀采样更有效识别人体情境。
Conventional pyroelectric infrared (PIR) motion sensors use paired elements for the detection of moving targets. This method makes them incapable of measuring thermal signals from static targets. We need an active sensor that can detect static thermal subjects. This paper presents our design of active PIR sensors. The proposed PIR sensing systems can actively detect static thermal targets by using three methods that are suitable to different applications: 1) a sensor that can be rotated by a self-controlled servo motor for the detection of moving or static thermal subjects nearby; 2) a sensor that is equipped with a mask for low-complexity posture recognition; and 3) a sensor that can be worn on the wrist for the recognition of surrounding subjects (this sensor is especially useful for blind users). Compressive sensing (CS) theory indicates that random down-sampling method can capture more accurate information of the original signal than the evenly spaced sampling. Based on CS theory, we have developed the random sampling structures for the active PIR systems, and have built a statistical feature space for human scenario recognition. The experimental results demonstrate that the active sensing system can efficiently measure the static thermal targets, and the random sampling scheme has a better recognition performance than the even sampling scheme.