Facing direction detection plays a critical role in human-computer interaction and has a wide application in surveillance systems, driving awareness recognition, smart home appliances, computer games, and so on. Curre...
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Facing direction detection plays a critical role in human-computer interaction and has a wide application in surveillance systems, driving awareness recognition, smart home appliances, computer games, and so on. Current detection methods are mainly focused on extracting specific patterns from user's optical images, which raises concerns on privacy invasion and these detection techniques do not usually work in a dark environment. To address these concerns, this paper proposes an activity recognition system guided by an unobtrusive sensor (ARGUS). By using a low pixel infrared thermopile arraysensor, ARGUS is capable of identifying five facing directions (left 45 degrees/90 degrees, right 45 degrees /90 degrees, and front) through the support vector machine classifier. Also two feature extraction methods are compared. One is manually-defined and the other is based on a pre-trained convolutional neural network (CNN) model. The facing direction detection accuracy resulting from manually-defined features reaches 85.3%, 90.6%, and 85.2% at detection distance of 0.6, 1.2, and 1.8 m, respectively. The level of accuracy resulted from using pre-trained CNN features demonstrates a much more reliable performance (89.1%, 95.3%, and 95.1% at distance of 0.6, 1.2, and 1.8 m, respectively). In addition, ARGUS has been successfully applied for occupancy tracking with a root mean square error of 0.19 m.
BackgroundPressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. ...
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BackgroundPressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the *** designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body *** experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to *** proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture.
With the successful launch of BIRD satellite in October 2001, new possibilities of the observation of hot events like forest fires, volcanic eruptions a.o. from space are opened. The BIRD (Bi-spectral infrared Detecti...
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ISBN:
(纸本)081944586X
With the successful launch of BIRD satellite in October 2001, new possibilities of the observation of hot events like forest fires, volcanic eruptions a.o. from space are opened. The BIRD (Bi-spectral infrared Detection) is the first satellite which is equipped with space instrumentation dedicated to recognize high temperature events. Current remote sensing systems have the disadvantage that they were not designed for the observation of hot events. Starting with the FIRES Phase A Study, the principle requirements and ideas for a fire recognition system were defined. With the German BIRD demonstrator mission, a feasible approach of these ideas has been realized and work now in space. This mission shall answer technological and scientific questions related to the operation of a compact bi-spectral infrared push-broom sensor and related to the detection and investigation of fires from space. The payload of BIRD is a multi-sensor system designed to fulfil the scientific requirements under the constraints of a micro satellite. The paper describes the basic ideas for fire detection and the estimation of fire temperature, fire size, and energy release in the sub-pixel domain and describes the technical solution for the infraredsensor system on board of BIRD.
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