Pyroelectric Infrared (PIR) Sensor Based Event Detection (original) (raw)
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Tracking Motion Direction and Distance With Pyroelectric IR Sensors
IEEE Sensors Journal, 2010
Passive infra-red (PIR) sensors are excellent devices for Wireless Sensor Networks (WSN), being low-cost, low-power and presenting a small form factor. PIR sensors are widely used as a simple, but reliable, presence trigger for alarms and automatic lighting systems. However, the output of a PIR sensor depends on several aspects beyond simple people presence, as for example distance of the body from the sensor, direction of movement and presence of multiple people. In this paper we present a feature extraction and sensor fusion technique that exploits a set of wireless nodes equipped with PIR sensors to track people moving in a hallway. Our approach has reduced computational and memory requirements, thus it is well suited for digital systems with limited resources, such those available in sensor nodes. Using the proposed techniques we were able to achieve 100% correct detection of direction of movement and 83.49% to 95.35% correct detection of distance intervals.
IRJET, 2020
The working of the Room Monitoring System Using PIR Sensor depends upon the infrared Radiation. The house security system becomes the best solution to overcome house intrusion problem when user is not in house. The infrared motion detector is capable to detect the motion while the PIR is capable to control the whole operation of the security system. Pyroelectric infrared (PIR) Sensor Module for human body detection circuit. High sensitivity and low noise output is a standard 5volt active low output signal. Module provides an optimized circuit that will detect motion up to 6 meters away and can be used in burglar alarms and access control system. Inexpensive and easy to use, it's ideal for alarm systems, motion-activated lightning, holiday props, and robotics applications. The output can be connected to microcontroller pin directly to monitor signal or a connected to transistor to drive DC loads like a bell, buzzer, siren, relay. The PIR sensor and Fresnel lens are fitted on to the PCB. This enables the board to be mounted inside a case with the detecting lens protruding outwards.
Pyroelectric InfraRed sensors based distance estimation
2008 IEEE Sensors, 2008
Pyroelectric InfraRed (PIR) sensors are low-power, low-cost devices commonly used in ambient monitoring systems in order to provide a simple, but reliable, trigger signal in presence of people. In this work we show how we are able to estimate the position of a person using PIR detectors. Our sensor node locally extracts basic features (passage duration and PIR's output amplitude) and fuses them from pairs of nodes in order to classify the passages into three classes according to person position. We tested three classifiers: Naïve Bayes, Support Vector Machines (SVM) and k-Nearest Neighbor (k-NN). All of them can be implemented on low power, low cost devices while achieving a correct classification ratio ranging from 80% up to 93%.
2011
This paper evaluates the development of a Low-cost security system using small PIR (Pyroelectric Infrared) sensor built around a microcontroller. The low-power PIR detectors take advantage of pyroelectricity to detect a human body that is a constant source of Passive Infrared (radiation in the infrared region). The system senses the signal generated by PIR sensor detecting the presence of individuals not at thermal equilibrium with the surrounding environment. Detecting the presence of any unauthorized person in any specific time interval, it triggers an alarm & sets up a call to a predefined number through a GSM modem. This highly reactive approach has low computational requirement, therefore it is well-suited to surveillance, industrial applications and smart environments. Tests performed gave promising results.
Human Movement Detection and Identification Using Pyroelectric Infrared Sensors
Pyroelectric infrared (PIR) sensors are widely used as a presence trigger, but the analog output of PIR sensors depends on several other aspects, including the distance of the body from the PIR sensor, the direction and speed of movement, the body shape and gait. In this paper, we present an empirical study of human movement detection and identification using a set of PIR sensors. We have developed a data collection module having two pairs of PIR sensors orthogonally aligned and modified Fresnel lenses. We have placed three PIR-based modules in a hallway for monitoring people; one module on the ceiling; two modules on opposite walls facing each other. We have collected a data set from eight subjects when walking in three different conditions: two directions (back and forth), three distance intervals (close to one wall sensor, in the middle, close to the other wall sensor) and three speed levels (slow, moderate, fast). We have used two types of feature sets: a raw data set and a reduced feature set composed of amplitude and time to peaks; and passage duration extracted from each PIR sensor. We have performed classification analysis with well-known machine learning algorithms, including instance-based learning and support vector machine. Our findings show that with the raw data set captured from a single PIR sensor of each of the three modules, we could achieve more than 92% accuracy in classifying the direction and speed of movement, the distance interval and identifying subjects. We could also achieve more than 94% accuracy in classifying the direction, speed and distance and identifying subjects using the reduced feature set extracted from two pairs of PIR sensors of each of the three modules.
Infra Red Radiation Detection using Passive Infrared Sensor
International Journal of Computer Applications
This paper proposes a new approach for infrared object localization and tracking with passive infrared sensors (PIR). The hierarchical architecture visibility of Fresnel lens away is presented with modulated field of view (FOV). The FOVs of lens array in sensor node are modulated to achieve a single degree of freedom (DOF). The energy imbalance problem effectively solve with the PIR system. PIR based system saves power consumption and memory space. Passive infrared system detects the change in the radiation of warm blood generation and completely used to turn On the webcam and lighting system.
Passive Infrared (PIR) Sensor Based Security System
IJEECS, 2013
In this paper, a PIR based security system which saves the power consumption and the memory space of the recording system has been proposed. Passive Infrared Radiation (PIR) sensor detects the change in infrared radiation of warm blooded moving object in its detection range. According to the change in infrared radiation, there will be a change in the voltages generated which was amplified and used to turn ON the webcam and lighting system through relay. Software was developed and installed in the computer to capture and record the video when the webcam gets turned ON. When an intruder comes in the detection range of the PIR sensor, it actuates the lighting system and the webcam. The software detects the webcam connection; it will start to record and save the video. Once the intruder moves out of detection range of the sensor, the webcam and light gets turn OFF. The software repeats the process. Thus the saves power consumption and the memory space of the recording system as the lamp and webcam will only get turned ON when PIR sensors detects an object. Consequently the system starts recording only when the webcam is turned ON; hence saving memory space.
Abnormal Activity Detection Using Pyroelectric Infrared Sensors
Sensors (Basel, Switzerland), 2016
Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted expe...