Browsing by Subject "Infrared sensors"

Now showing 1 - 19 of 19

Open Access
A comparative analysis of different approaches to target differentiation and localization using infrared sensors
(2006) Aytaç, Tayfun
This study compares the performances of various techniques for the differentiation and localization of commonly encountered features in indoor environments, such as planes, corners, edges, and cylinders, possibly with different surface properties, using simple infrared sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and differentiation process. The techniques considered include rule-based, template-based, and neural network-based target differentiation, parametric surface differentiation, and statistical pattern recognition techniques such as parametric density estimation, various linear and quadratic classifiers, mixture of normals, kernel estimator, k-nearest neighbor, artificial neural network, and support vector machine classi- fiers. The geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. For a set of six surfaces, we get a correct differentiation rate of 100% in parametric differentiation based on reflection modeling. The results demonstrate that simple infrared sensors, when coupled with appropriate processing, can be used to extract substantially more information than such devices are commonly employed for. The demonstrated system would find application in intelligent autonomous systems such as mobile robots whose task involves surveying an unknown environment made of different geometry and surface types. Industrial applications where different materials/surfaces must be identified and separated may also benefit from this approach.
Open Access
Differentiation and localization of target primitives using infrared sensors
(IEEE, 2002-09-10) Aytaç, Tayfun; Barshan, Billur
This study investigates the use of low-cost infrared sensors in the differentiation and localization of commonly encountered target primitives in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings from such sensors are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6°, respectively. The proposed method should facilitate the use of infrared sensors in mobile robot applications for differentiation and localization beyond their common usage as simple proximity sensors for object detection and collision avoidance.
Open Access
Differentiation and localization of targets using infrared sensors
(Elsevier, 2002) Aytaç, T.; Barshan, B.
This study investigates the use of low-cost infrared emitters and detectors in the differentiation and localization of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings obtained with such systems are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6°, respectively. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than that which they are commonly employed for.
Open Access
Differentiation and localization using infrared sensors
(2002-08) Aytaç, Tayfun
In this thesis different approaches for the differentiation and localization of targets using low cost infrared sensors are presented. The intensity readings obtained with such sensors are highly dependent on the location and properties of targets in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. We propose the use of angular intensity scans and present different approaches to process them. Using these approaches, targets of different geometrical shapes but identical surface properties targets of difffferent surface properties but identical geometry, and targets having both different geometrical shapes and surface properties are differentiated and localized in a position-invariant manner. Maximum correct differentiation rates of 97% 87% and 65% are respectively achieved in these cases, indicating that the geometrical properties of targets are more distinctive than their surface properties in the differentiation process. The different approaches are verified experimentally with target types of commonly encountered geometries in indoor environments and with surfaces of different reflection properties. The results indicate that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than they are commonly employed for.
Open Access
Falling person detection using multi-sensor signal processing
(IEEE, 2007) Töreyin, Behçet Uğur; Soyer, Emin Birey; Onaran, İbrahim; Çetin, A. Enis
Falls are one of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. In this paper, signals produced by sound and passive infrared (PIR) sensors are simultaneously analyzed to detect suddenly falling elderly people. A typical room in a supportive home can be equipped with sound and PIR sensors. Hidden Markov models are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs can be fused together to reach a final decision.
Open Access
Hand gesture based remote control system using infrared sensors and a camera
(Institute of Electrical and Electronics Engineers, 2014) Erden, F.; Çetin, A.
In this paper, a multimodal hand gesture detection and recognition system using differential Pyroelectric Infrared (PIR) sensors and a regular camera is described. Any movement within the viewing range of the differential PIR sensors are first detected by the sensors and then checked if it is due to a hand gesture or not by video analysis. If the movement is due to a hand, one-dimensional continuous-time signals extracted from the PIR sensors are used to classify/recognize the hand movements in real-time. Classification of different hand gestures by using the differential PIR sensors is carried out by a new winner-takeall (WTA) hash based recognition method. Jaccard distance is used to compare the WTA hash codes extracted from 1-D differential infrared sensor signals. It is experimentally shown that the multimodal system achieves higher recognition rates than the system based on only the on/off decisions of the analog circuitry of the PIR sensors.
Open Access
Improved range estimation using simple infrared sensors without prior knowledge of surface characteristics
(Institute of Physics Publishing Ltd., 2005) Yüzbaşioglu, Ç.; Barshan, B.
This paper describes a new method for position estimation of planar surfaces using simple, low-cost infrared sensors. The intensity data acquired with infrared sensors depend highly on the surface properties and the configuration of the sensors with respect to the surface. Therefore, in many related studies, either the properties of the surface are determined first or certain assumptions about the surface are made in order to estimate the distance and the orientation of the surface relative to the sensors. We propose a novel method for position estimation of surfaces with infrared sensors without the need to determine the surface properties first. The method is considered to be independent of the type of surface encountered since it is based on searching for the position of the maximum value of the intensity data rather than using absolute intensity values which would depend on the surface type. The method is verified experimentally with planar surfaces of different surface properties. An intelligent feature of our system is that its operating range is made adaptive based on the maximum intensity of the detected signal. Three different ways of processing the intensity signals are considered for range estimation. The absolute mean range error for the method resulting in the lowest errors is 0.15 cm over the range from 10 to 50 cm. The cases where the azimuth and elevation angles are nonzero are considered as well. The results obtained demonstrate that infrared sensors can be used for localization to an unexpectedly high accuracy without prior knowledge of the surface characteristics.
Open Access
İstatistiksel örüntü tanıma teknikleri kullanarak kızılberisi algılayıcılarla hedef ayırdetme
(IEEE, 2006-04) Aytaç, Tayfun; Yüzbaşıoǧlu, Çağrı; Barshan, Billur
This study compares the performances of different statistical pattern recognition techniques to differentiation of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders, using low-cost infrared sensors. The pattern recognition techniques compared include parametric density estimation, mixture of Gaussians, kernel estimator, k-nearest neighbor classifier, neural network classifier, and support vector machine classifier. A correct differentiation rate of 100% is achieved for six surfaces using parametric differentiation. For three geometries covered with seven different surfaces, best correct differentiation rate (100%) is achieved with mixture of Gaussians classifier with three components. The results demonstrate that simple infrared sensors, when coupled with appropriate processing, can be used to extract substantially more information than such devices are commonly employed. © 2006 IEEE.
Open Access
Position-invariant surface recognition and localization using infrared sensors
(SPIE, 2003) Barshan, B.; Aytaç, T.
Low-cost infrared emitters and detectors are used for the recognition of surfaces with different properties in a location-invariant manner. The intensity readings obtained with such devices are highly dependent on the location and properties of the surface in a way that cannot be represented in a simple manner, complicating the recognition and localization process. We propose the use of angular intensity scans and present an algorithm to process them. This approach can distinguish different surfaces independently of their positions. Once the surface is identified, its position can also be estimated. The method is verified experimentally with the surfaces aluminum, white painted wall, brown kraft paper, and polystyrene foam packaging material. A correct differentiation rate of 87% is achieved, and the surfaces are localized within absolute range and azimuth errors of 1.2 cm and 1.0 deg, respectively. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than they are commonly employed for. © 2003 Society of Photo-Optical Instrumentation Engineers.
Open Access
Range estimation using simple infrared sensors without prior knowledge of surface parameters
(IEEE, 2004) Yüzbaşıoğlu, Çağrı; Barshan, Billur
This paper describes a new method for range estimation using low-cost infrared sensors. The intensity data obtained with infrared sensors depends highly on the surface properties and the configuration of the sensors and the surface. Therefore, in many of the related studies, either the properties of the surface are determined first or certain assumptions about the surface are made in order to calculate the distance and the orientation of the surface relative to the sensors. In this paper, we propose a novel method for position estimation of surfaces with infrared sensors without the need to determine the surface properties first. The method is verified experimentally with planar surfaces covered with white paper, wooden block, bubbled packing material, white styrofoam, blue and brown cardboard. The overall absolute mean error in the range estimates has been calculated as 0.21 cm in the range from 12.5 to 45 cm. The results obtained demonstrate that infrared sensors can be easily used for localization to an unexpectedly high accuracy without prior knowledge of the surface parameters.
Open Access
A robust system for counting people using an infrared sensor and a camera
(Elsevier BV, 2015) Erden, F.; Alkar, A. Z.; Çetin, A. Enis
In this paper, a multi-modal solution to the people counting problem in a given area is described. The multi-modal system consists of a differential pyro-electric infrared (PIR) sensor and a camera. Faces in the surveillance area are detected by the camera with the aim of counting people using cascaded AdaBoost classifiers. Due to the imprecise results produced by the camera-only system, an additional differential PIR sensor is integrated to the camera. Two types of human motion: (i) entry to and exit from the surveillance area and (ii) ordinary activities in that area are distinguished by the PIR sensor using a Markovian decision algorithm. The wavelet transform of the continuous-time real-valued signal received from the PIR sensor circuit is used for feature extraction from the sensor signal. Wavelet parameters are then fed to a set of Markov models representing the two motion classes. The affiliation of a test signal is decided as the class of the model yielding higher probability. People counting results produced by the camera are then corrected by utilizing the additional information obtained from the PIR sensor signal analysis. With the proof of concept built, it is shown that the multi-modal system can reduce false alarms of the camera-only system and determines the number of people watching a TV set in a more robust manner.
Open Access
Rule-based target differentiation and position estimation based on infrared intensity measurements
(SPIE, 2003) Aytaç, T.; Barshan, B.
This study investigates the use of low-cost infrared sensors in the differentiation and localization of target primitives commonly encountered in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings from such sensors are highly dependent on target location and properties in a way that cannot be represented in a simple manner, making the differentiation and localization difficult. We propose the use of angular intensity scans from two infrared sensors and present a rule-based algorithm to process them. The method can achieve position-invariant target differentiation without relying on the absolute return signal intensities of the infrared sensors. The method is verified experimentally. Planes, 90-deg corners, 90-deg edges, and cylinders are differentiated with correct rates of 90%, 100%, 82.5%, and 92.5%, respectively. Targets are localized with average absolute range and azimuth errors of 0.55 cm and 1.03 deg. The demonstration shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than they are commonly employed for.
Open Access
Simultaneous extraction of geometry and surface properties of targets using infrared intensity signals
(IEEE, 2005) Aytaç, Tayfun; Barshan, Billur
We propose the use of angular intensity signals obtained with low-cost infrared sensors and present an algorithm to simultaneously extract the geometry and surface properties of commonly encountered targets in indoor environments. The method is verified experimentally with planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1°, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99% and 81%, respectively, indicating that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple infrared sensors, when coupled with appropriate signal processing, can be used to extract substantially more information than such devices are commonly employed for.
Open Access
Simultaneous extraction of geometry and surface properties of targets using simple infrared sensors
(SPIE, 2004) Aytaç, T.; Barshan, B.
We investigate the use of low-cost infrared (IR) sensors for the simultaneous extraction of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We propose the use of angular intensity scans and present an algorithm to process them to determine the geometry and the surface type of the target and estimate its position. The method is verified experimentally with planes, 90-deg corners, and 90-deg edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1 deg, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99 and 81%, respectively, which shows that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to extract substantially more information than that for which such devices are commonly employed. © 2004 Society of Photo-Optical Instrumentation Engineers.
Open Access
Surface differentiation and localization by parametric modeling of infrared intensity scans
(IEEE, 2005) Aytaç, Tayfun; Barshan, Billur
In this study, surfaces with different properties are differentiated with simple low-cost infrared (IR) emitters and detectors in a location-invariant manner. The intensity readings obtained from such sensors are highly dependent on the location and properties of the surface, which complicates the differentiation and localization process. Our approach, which models IR intensity scans parametrically, can distinguish different surfaces independent of their positions. The method is verified experimentally with wood, Styrofoam packaging material, white painted wall, white and black cloth, and white, brown, and violet paper. A correct differentiation rate of 100% is achieved for six surfaces and the surfaces are localized within absolute range and azimuth errors of 0.2 cm and 1.1°, respectively. The differentiation rate decreases to 86% for seven surfaces and to 73% for eight surfaces. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to differentiate different types of surfaces in a location-invariant manner.
Open Access
Surface differentiation and position estimation by parametric modeling of signals obtained with infrared sensors
(IEEE, 2004) Aytaç, Tayfun; Barshan, Billur
In this study, low-cost infrared emitters and detectors are used for the recognition of surfaces with different properties in a location-invariant manner. The intensity readings obtained with such sensors are highly dependent on the location and properties of the surface in a way that cannot be represented analytically in a simple manner, complicating the differentiation and localization process. Our approach, which models infrared angular intensity scans parametrically, can distinguish different surfaces independently of their positions. Once the surface type is identified, its position can also be estimated. The method is verified experimentally with wood, styrofoam packaging material, white painted wall, white and black clothes, and white, brown, and violet papers. A correct differentiation rate of 73% is achieved over eight surfaces and the surfaces are localized within absolute range and azimuth errors of 0.8 cm and 1.1°, respectively. The differentiation rate improves to 86% over seven surfaces and 100% over six surfaces. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than they are commonly employed for.
Open Access
Surface differentiation by parametric modeling of infrared intensity scans
(SPIE-International Society for Optical Engineering, 2005) Aytaç, T.; Barshan, B.
We differentiate surfaces with different properties with simple low-cost IR emitters and detectors in a location-invariant manner. The intensity readings obtained with such sensors are highly dependent on the location and properties of the surface, which complicates the differentiation and localization process. Our approach, which models IR intensity scans parametrically, can distinguish different surfaces independent of their positions. Once the surface type is identified, its position (r, θ) can also be estimated. The method is verified experimentally with wood; Styrofoam packaging material; white painted matte wall; white and black cloth; and white, brown, and violet paper. A correct differentiation rate of 100% is achieved for six surfaces, and the surfaces are localized within absolute range and azimuth errors of 0.2 cm and 1.1 deg, respectively. The differentiation rate decreases to 86% for seven surfaces and to 73% for eight surfaces. The method demonstrated shows that simple IR sensors, when coupled with appropriate signal processing, can be used to recognize different types of surfaces in a location-invariant manner.
Open Access
Target differentiation and localization using infrared sensors
(SPIE, 2003-08) Aytaç, Tayfun; Barshan, Billur
We discuss the use of low-cost infrared sensors in differentiating and localizing commonly encountered target primitives in indoor environments, such as planes, corners, edges, and cylinders. Single intensity readings are highly dependent on target location and properties and this dependence cannot be represented simply. We propose a method that can achieve position-invariant target differentiation without relying on absolute intensity readings and verify it experimentally. The correct identification rates for planes, 90° corners and edges, and cylinders are 90%, 100%, 82.5%, and 92.5%, respectively. The distance of the target can be estimated with an average error of 0.59 cm and the azimuth angle can be estimated with an error of 1.58°.
Open Access
Target differentiation with simple infrared sensors using statistical pattern recognition techniques
(Elsevier BV, 2007) Barshan, B.; Aytaç, T.; Yüzbaşıoğlu, Ç.
This study compares the performances of various statistical pattern recognition techniques for the differentiation of commonly encountered features in indoor environments, possibly with different surface properties, using simple infrared (IR) sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the differentiation process. We construct feature vectors based on the parameters of angular IR intensity scans from different targets to determine their geometry and/or surface type. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. Parametric differentiation correctly identifies six different surface types of the same planar geometry, resulting in the best surface differentiation rate (100%). However, this rate is not maintained with the inclusion of more surfaces. The results indicate that the geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. The results demonstrate that simple IR sensors, when coupled with appropriate processing and recognition techniques, can be used to extract substantially more information than such devices are commonly employed for.