Preprocessing Design in Pyroelectric Infrared Sensor-Based Human-Tracking System: On Sensor Selection and Calibration

Lu, Jiang; Zhang, Ting; Hu, Fei; Hao, Qi

doi:10.1109/tsmc.2016.2523914

Cited by 36 publications

(13 citation statements)

References 25 publications

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“…Shen [ 19 ] proposed a design, considering the FOV of a PIR detector with a hemispherical Fresnel lens array in a cone-shape space, and its projection on the ground plane is an area with several concentric rings. Lu [ 20 ] illustrated the tracking experimental settings, where the experiments are conducted in a 6 m × 6 m observation space and sixteen PIR sensor nodes (32 sensors) are deployed on four walls with pegboards. The Fresnel lens arrays and coded masks were applied to subdivide the FOV of each sensor and encode the sub-regions.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Networked PIR Detection System for Indoor Human Localization

Chen

Wen

et al. 2021

Sensors

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Pyroelectric Infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems can be categorized as wearable and non-wearable systems, where the latter are also known as device-free localization systems. Since the binary PIR sensor detects only the presence of a human motion in its field of view (FOV) without any other information about the actual location, utilizing the information of overlapping FOV of multiple sensors can be useful for localization. In this study, a PIR detector and sensing signal processing algorithms were designed based on the characteristics of the PIR sensor. We applied the designed PIR detector as a sensor node to create a non-wearable cooperative indoor human localization system. To improve the system performance, signal processing algorithms and refinement schemes (i.e., the Kalman filter, a Transferable Belief Model, and a TBM-based hybrid approach (TBM + Kalman filter)) were applied and compared. Experimental results indicated system stability and improved positioning accuracy, thus providing an indoor cooperative localization framework for PIR sensor networks.

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Section: Related Workmentioning

confidence: 99%

Cooperative Networked PIR Detection System for Indoor Human Localization

Chen

Wen

et al. 2021

Sensors

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“…In addition, compared with PIRATES, the estimation accuracies of DeepPIRATES are are 0.91 degree, 1.24 degree, and the 1.38 degree higher than the method of PIRATES in 1-person, 2-person, and 3person scenarios, respectively. Moreover, since the methods of PIRNet, [6] and [7] do not contain the step of azimuth change estimation, we will only compare DeepPIRATES with them in the respect of localization accuracy in the next subsection. Fig.…”

Section: B Accuracy Of Azimuth Change Estimationmentioning

confidence: 99%

“…Besides, as shown in Table 2, compared with the traditional methods [6] and [7], although the localization accuracy of DeepPIRATES still has room to be improved, DeepPI-RATES has a much lower deployment density than them and is more practical in scenarios like smart home which are sensitive to the system cost but do not require high localization accuracy.…”

Section: Localization Accuracymentioning

confidence: 99%

DeepPIRATES: A Training-Light PIR-Based Localization Method With High Generalization Ability

et al. 2021

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Pyroelectric infrared (PIR) sensors are much promising for device-free localization (DFL) due to their advantages of lower cost, low power consumption, and privacy protection. Most PIR-based localization methods usually assume some geometric models according to the detection principle of PIR sensors, which are however not accurate or robust due to the various cases of infrared radiation from human body, especially the case of multiple persons. Recently, deep learning is utilized in the PIR-based localization method (i.e. PIRNet [1]) and well handles the complex infrared radiation even in the multiperson case. However, this method requires a high training cost, and has very weak generalization ability as it assumes the PIR sensors' deployment in the testing environment is same to the deployment in training environment. To reduce the training cost and achieve high generalization ability, in this paper, we propose a robust method DeepPIRATES, which can be directly utilized in various deployment scenarios without retraining. DeepPIRATES combines deep learning and a geometric model. Specifically, DeepPIRATES divides the localization task into two steps. The first step utilizes a neural network to estimate the azimuth changes of multiple persons to a PIR sensor. Then, DeepPIRATES utilizes the persons' azimuth changes to infer their locations based on a geometric model. Extensive experimental results show that DeepPIRATES can achieve similar localization accuracy as PIRNet, while does not require to be retrained when the sensor deployment changes.

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“…On the other hand, the works [11]- [13] introduced how to track multiple persons. The relationship between the deployment and the localization accuracy is discussed in [14]- [16]. In addition, the works [17] and [18] discussed how to integrate the PIR sensor's binary information with other kinds of sensors for localization.…”

Section: Related Workmentioning

confidence: 99%

A new PIR-based method for real-time tracking

Yang¹,

Li²,

Tang³

et al. 2019

Preprint

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Pyroelectric infrared (PIR) sensors are considered to be promising devices for device-free localization due to its advantages of low cost, less intrusive, and the immunity from multi-path fading. However, most of the existing PIR-based localization systems only utilize the binary information of PIR sensors and therefore require a large number of PIR sensors and a careful deployment. A few works directly map the raw data of PIR sensors to one's location using machine learning approaches. However, these approaches require to collect abundant training data and suffer from environmental change. In this paper, we propose a PIR-based device-free localization approach based on the raw data of PIR sensors. The key of this approach is to extract a new type of location information called as the azimuth change. The extraction of the azimuth change relies on the physical properties of PIR sensors. Therefore, no abundant training data are needed and the system is robust to environmental change. Through experiments, we demonstrated that a device-free localization system incorporating the information of azimuth change outperforms the state-of-the-art approaches in terms of higher location accuracy. In addition, the information of the azimuth change can be easily integrated with other PIR-based localization systems to improve their localization accuracy.

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Preprocessing Design in Pyroelectric Infrared Sensor-Based Human-Tracking System: On Sensor Selection and Calibration

Cited by 36 publications

References 25 publications

Cooperative Networked PIR Detection System for Indoor Human Localization

Cooperative Networked PIR Detection System for Indoor Human Localization

DeepPIRATES: A Training-Light PIR-Based Localization Method With High Generalization Ability

A new PIR-based method for real-time tracking

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