WO2021182983A1 - Système de détection de la présence d'objets avec un contrôleur automatique - Google Patents

Système de détection de la présence d'objets avec un contrôleur automatique Download PDF

Info

Publication number
WO2021182983A1
WO2021182983A1 PCT/RU2020/000133 RU2020000133W WO2021182983A1 WO 2021182983 A1 WO2021182983 A1 WO 2021182983A1 RU 2020000133 W RU2020000133 W RU 2020000133W WO 2021182983 A1 WO2021182983 A1 WO 2021182983A1
Authority
WO
WIPO (PCT)
Prior art keywords
light
self
detecting
objects
spectrum
Prior art date
Application number
PCT/RU2020/000133
Other languages
English (en)
Russian (ru)
Inventor
Сергей Станиславович ЧАЙКОВСКИЙ
Original Assignee
Сергей Станиславович ЧАЙКОВСКИЙ
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Сергей Станиславович ЧАЙКОВСКИЙ filed Critical Сергей Станиславович ЧАЙКОВСКИЙ
Publication of WO2021182983A1 publication Critical patent/WO2021182983A1/fr

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/181—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using active radiation detection systems
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G08B21/22—Status alarms responsive to presence or absence of persons

Definitions

  • This invention relates to the field of computing, and, in particular, to methods and systems for detecting the presence of objects with a self-monitoring function.
  • Biometric systems are one of the most promising solutions, devoid of these disadvantages. Such systems are based on the analysis of biometric information about the user: facial features, voice, gestures, fingerprints, etc. The biometric parameters of the user, automatically read by the system, are compared with the templates, stored in the database. If one of the templates matches the received data, then the user is considered identified and he is allowed access.
  • biometric systems are based on the analysis of the following biometric parameters of a person: face, voice, iris, gestures. Other types of parameters either do not provide sufficient accuracy for user identification or require contact with the reader (as is the case with fingerprints).
  • the existing biometric systems have a number of disadvantages that allow unauthorized access to the protected area.
  • the invention relates to security and control systems. Its use makes it possible to obtain a technical result in the form of increased reliability and speed in detecting attempts of unauthorized access to an object. This is achieved due to the fact that detection of the face of a living person and detection of unauthorized users present near the registered user are used as the main mechanism.
  • the invention uses methods for tracking a three-dimensional object reduced to the first normalized face shape, using a fast method of measuring and comparing facial expressions with a template, as well as methods for detecting local features and presenting a face in three different normalized forms.
  • a fast method of measuring and comparing with a template such a behavioral biometric characteristic as a phonemic signature is used, which is obtained as a result of the user's execution of system commands.
  • the technical problem or technical problem solved in this technical solution is the implementation of a system for detecting the presence of objects with self-control.
  • the technical result is to improve the quality of detection of the presence of objects through the use of artificial intelligence, as well as the use of a sensitive means to light of a predetermined spectrum.
  • the specified technical result is achieved by implementing a system for detecting the presence of objects with self-monitoring, which contains at least one light source made with the ability to emit light of a predetermined spectrum, each of the sources having a different predetermined spectrum for each predetermined area; at least one light sensing means of a predetermined spectrum, configured to detect light reflected from at least one object, if present in a predetermined area, and to generate a presence signal based on the detected light, and at least one processor a device for determining the presence of an object based on a presence signal generated in a previous step, the processor inferring the presence of an object in a predetermined area based on the presence signals.
  • the sensing means comprises a photosensor that is sensitive to light of a predetermined spectrum.
  • the sensing means comprises a photosensor equipped with a spectral filter for filtering light of a predetermined spectrum.
  • two light sources are contained in one light emitting device, the light emitting device being configured to emit light from at least two different predetermined spectra in two zones.
  • the first spectrum and the second spectrum do not substantially overlap.
  • the object is a human and the presence signal represents a human vital signal.
  • the presence signal represents the vital signals of at least two people present in the same area, and in which the processing device distinguishes the corresponding vital signals from at least two people present in the area.
  • the target spectrum emitted by the light sources comes from the visible spectrum and / or.
  • each of the photosensors includes a photodiode.
  • the photodiodes are located together in a photodiode array.
  • FIG. 1 schematically shows a first exemplary embodiment of a presence detection system in which an area comprises two zones.
  • FIG. 2 schematically shows the intensity of presence signals measured by the presence detection system according to FIG. 1.
  • FIG. 3 schematically shows a second exemplary embodiment of a presence detection system in which an area comprises three zones.
  • the system means a computer system, a computer (electronic computer), CNC (numerical control), PLC (programmable logic controller), computerized control systems and any other devices capable of performing a given, well-defined sequence of operations. (actions, instructions), centralized and distributed databases, smart contracts.
  • a command processing device an electronic unit or an integrated circuit (microprocessor) executing machine instructions (programs), or the like.
  • a command processor reads and executes machine instructions (programs) from one or more storage devices.
  • Data storage devices can be, but are not limited to, hard disks (HDD), flash memory, ROM (read-only memory), solid state drives (SSD), optical drives.
  • a program is a sequence of instructions for execution by a computer control device or command processing device.
  • Server an electronic device that performs service functions at the request of the client, providing him with access to certain resources.
  • a server is contemplated that has a persistent connection to the internetwork that can transmit data to a client device. The server can process this data and transmit the processing result back to the client device.
  • a data exchange module is a server module that can represent a receiver of incoming signals, and a converter for further processing, and a translator for further sending.
  • a compute module is a server module that is a microprocessor specially adapted for complex signal processing.
  • Fig. 1 is a schematic diagram of a first exemplary embodiment of an object presence detection system according to the invention.
  • the system comprises two light sources, a first light source 110.1 and a second light source 110.2 for emitting light of predetermined spectra, and two sensing means comprising two photosensors in this embodiment, namely a first photosensor 120.1 and a second photosensor 120.2.
  • the photosensors are equipped with spectral filters, a first spectral filter 130.1 and a second spectral filter 130.2, respectively, for filtering light of predetermined spectra.
  • the photosensors 120.1, 120.2 are configured to detect light reflected from a person 140 present in the area that has passed through the respective spectral filters 130.1, 130.2, and to generate two presence signals based on the detection results.
  • a detector system can be connected to a system of a type other than a lighting system, such as an HVAC system.
  • the photosensor may include various types of optical sensors that provide means for converting the detected optical radiation into an electrical signal
  • the photosensor may be a broadband sensor or a narrowband sensor.
  • the photosensor may be a phototransistor, photosensitive integrated circuit, an off-power LED, a silicon photodiode, or other device.
  • the broadband sensor is aligned with the filter, and thus a narrow optical detection bandwidth can be provided.
  • One or more photosensors are generally configured to generate electrical signals representative of the respective output radiation of one or more light emitting elements, which can then be used in a controller such as a signal processor or control system (e.g., controller, microcontroller, software and / or device and so on), or other such control means for estimating the output radiation of the light source and, if necessary, adjusting the corresponding output radiation of one or more light-emitting elements.
  • a controller such as a signal processor or control system (e.g., controller, microcontroller, software and / or device and so on), or other such control means for estimating the output radiation of the light source and, if necessary, adjusting the corresponding output radiation of one or more light-emitting elements.
  • the photosensor may comprise a charge coupled device (CCD, CCD) with spectrally sensitive detectors for converting the energy of the laser beam into digital signals, which can then be processed by a processing unit. subsystem.
  • CCD charge coupled device
  • the charge coupled device can be any device capable of transferring electrical charge from the device to a location where the charge can be processed, such as by converting to a digital value for processing by a processing subsystem, obtained by "displacing" the signals one by one between steps in the device.
  • a charge coupled device can move charge between capacitive cells in a device using bias to transfer charge between cells.
  • a CCD device may include n-well / p-sub photodiodes, a capacitive current-controlled voltage amplifier, pixel scanners, and delta-differentiating circuits. Using a charge coupled instrument can eliminate the need for a discrete spectrometer and current-to-voltage converter.
  • Presence signals are provided to a processor 150, wherein the processor 150 determines the presence or absence of a person 140 based on the provided presence signals 160.1; 160.2.
  • An area can be covered by two zones, a first zone 170.1 and a second zone 170.2.
  • Each of the two light sources, the first light source 110.1 and the second light source 110.2 has a different predetermined spectrum, and each emits light within a different area, the first area 170.1 and the second area 170.2, respectively.
  • Each of the two photosensors, the first photosensor 120.1 and the second photosensor 120.2 is configured to operate with one of the light sources, the first light source 110.1 and the second light source 110.2, respectively, and together with a corresponding spectral filter, the first spectral filter 130.1 and the second spectral filter 130.2 are accordingly configured to detect corresponding light of a predetermined spectrum.
  • the processor 150 infers the presence of a person 140 by zone.
  • a spectral filter can be additionally installed behind the focusing optics to allow only a certain spectrum of wavelengths to pass through.
  • the sensing means comprise photosensors having different spectral sensitivities.
  • a photosensor sensitive to light of a predetermined spectrum, can be use instead of a photosensor equipped with a spectral filter to filter light from a predetermined spectrum.
  • Photosensors must have different spectral sensitivity.
  • the photosensors may have fundamentally different spectral sensitivities so that they can inherently discriminate between light from different predefined spectra. Substantially different spectral sensitivities can be realized, for example, by using different classes of photosensors, such as photodiodes, CCDs, photomultipliers, etc., without limitation.
  • spectral sensitivities can be realized using photosensors of the same class, such as photodiodes, with different material properties, for example, based on silicon (Si) or based on gallium arsenide (GaAs), different doping levels in silicon, etc.
  • different spectral sensitivities can be realized by adding spectral filters.
  • the first person 140 is positioned in the first area 170.1 in the area.
  • the first photosensor 120.1 detects light emitted from the first light source 110.1, reflected from the first person 140 and is filtered by the first spectral filter 130.1, while the second photosensor 120.2 does not detect any significant light.
  • the processor 150 receives two presence signals 160.1; 160.2, wherein the first presence signal 160.1 indicates the presence of a person in the first zone 170.1, and the second presence signal 160.2 indicates the absence of a person in the second zone 170.2. Therefore, the processing unit 150 detects the presence of a person in the zone 170 and, in particular, the presence in the first zone 170.1.
  • Photosensors such as photodiodes, are capable of measuring vital signals from a person.
  • Photoplethysmography is the monitoring of changes in the blood pulse in the capillaries of a person through the absorption of light by oxydeoxyhemoglobin.
  • Photoplethysmography can be performed using simple photosensors such as photodiodes.
  • a photodiode monitors tiny changes in light intensity caused by cycling the passage of fresh blood in the capillaries of the skin. With such a photodiode, it is possible to dynamically measure the heart rate, heart rate variability, oxygenation of the blood and possibly the blood pressure of a person at a distance of several meters.
  • photodiodes are capable of measuring vital signals such as heart rate over a wide range of spectral frequencies. Most preferably, the system uses light in the invisible range of the spectrum, making the measurement completely invisible.
  • presence signals 160.1; 160.2 can represent a human heart rate signal. This feature provides additional security that the presence of a person is correctly determined, since the known parameters of the heart rate signal can be used by the processor as an additional criterion for inferring the presence of a person.
  • Spectral filters can be very simple components, such as deposited thin films, that are located directly on top of the photosensors, ie, photodiodes.
  • Photodiodes can be manufactured using standard semiconductor silicon technology or, alternatively, can be manufactured using amorphous silicon technology, which is used to make LCD displays on glass or flexible plastic substrates.
  • amorphous silicon technology which is used to make LCD displays on glass or flexible plastic substrates. The latter technology mentioned has the advantage of lower cost and larger diode area.
  • Light sources 110.1, 110.2 can also emit light in more than one area and instead emit light of relatively high intensity in the main area, and light of relatively low intensity in adjacent areas of the main area.
  • the first light source 110.1 emits light of relatively high intensity in the first zone 170.1, as well as light of relatively low intensity in the second zone 170.2, and similarly, the light source 110.2 emits light of relatively high intensity in the second zone 170.2, as well as a relatively low intensity in the first zone 170.1.
  • Light of the first light source 110.1 continuously decreases from the main area, in this example from the first area 170.1, to areas further away from the main area. Such a feature can be used to determine a relatively more accurate position of the first person 140 within the area.
  • the heartbeat of the first person 140 will be detected by the first photosensor 120.1 relatively strongly and relatively weakly by the second photosensor 120.2.
  • the heartbeat of the second person 140.1 being detected by the first photosensor 120.1 as a relatively strong signal, approximately the same intensity as for the first person 140, and as a relatively weak signal detected by the second photosensor 120.2, but still with a higher intensity than the first person 140.
  • This is represented by the table shown in FIG. 2.
  • this feature can be used to determine a more accurate position of a person in a zone.
  • heart rate measurements for first person 140 and second person 140.1 will indicate that second person 140.1 is closer to second zone 170.2 than first person 140, while it will be apparent that both people are in zone 170.1.
  • the number of zones in area 170 can be different, virtually any integer greater than one. Increasing the number of zones can improve the accuracy of the presence detection system. However, the number of zones should also be selected to match the size of the area 170, namely a relatively small area can be very well covered with just two areas, while a relatively large area may require more than 2 areas.
  • FIG. 3 schematically shows a second exemplary embodiment of a presence detection system in accordance with the invention, in which the system is used to detect the presence of more than one person, in this particular example, the presence of three people, the first person 140, the second person 140.1 and the third person 140.2.
  • the processor 150 will receive three presence signals, a first presence signal 160.1, a second presence signal 160.2, and a third presence signal 160.3. These presence signals will indicate the presence three people.
  • the first presence signal 160.1 will indicate the presence of the first person 140 having a first heart rate.
  • a third presence signal 160.3 will indicate the presence of a second person 140.1 and a third person 140.2 having a second heart rate and a third heart rate.
  • the system according to the invention is capable of detecting more than one person in different areas as well as in the same area. This is possible because the presence signal contains information about different heartbeats found in different people in the same area. To confirm that two or more people are actually present in the same area, any difference in the measured heart rates must be determined, such as different heart rates, heart rate variability, etc.
  • a neural network is a graph of interconnected non-linear processing units (processors) that can be trained to approximate complex mappings between input data and output data.
  • the input data is, for example, a digital presence signal (a set of coordinates of a living object)
  • the output is, for example, a classification solution (in the simplest case, + 1 / -1, which means "yes", there is a person in the signal, or "no", there is no person in the signal).
  • Each nonlinear processor (or neuron) consists of a weighted linear combination of its inputs, to which a nonlinear activation function is applied.
  • a neural network is defined by its connectivity structure, its nonlinear activation function and its weights.
  • a concept is used that may be called and is called relevance propagation in the following description. It redistributes the evidence (basis) for a particular structure in the data, as modeled by the output neurons, back to the input neurons. So she seeks to explain its own prediction in terms of input variables (e.g. coordinates of a living object). Note that this concept works for any type (no loop) neural network, regardless of the number of layers, type of activation function, etc. Thus, it can be applied to many popular models, since many algorithms can be described in terms of neural networks.
  • An artificial neural network is made up of neurons. Neurons are interconnected with each other or interact with each other. Typically, each neuron is connected to downstream (downstream) adjacent (or downstream) neurons on one side and upstream (upstream) neighboring (or upstream) neurons on the other side.
  • upstream refers to the general direction of propagation along which the neural network operates when applied to a set of elements to map the set of elements to the output of the network, that is, to perform prediction.
  • the set of elements may, for example, be a set of image pixels that form an image by associating each pixel with a pixel value corresponding to the color or intensity of the scene, at a spatial location corresponding to the position of the corresponding pixel in the image pixel array, or for example, presence signal data that contains information about the different heartbeats of people.
  • the set is an ordered set of items, namely an array of pixels or heart rate data.
  • the elements will correspond to individual pixel values, i.e. each element will correspond to one pixel. It will be further explained that the present application is not limited to the field of images. Rather, a collection of elements can be a collection of elements without any ordering defined among the elements. Combinations between them can also take place.
  • the first or lowest layer of neurons forms a kind of input to the artificial neural network. That is, each neuron of this lower layer takes as input at least a subset of the set of elements, that is, at least a subset of pixel values. The union of subsets of elements from the set, the values of which are entered into some neuron of the lower layer. In other words, for each element of the set, its value is entered into at least one of the neurons of the lower layer.
  • the network On the opposite side of the neural network, that is, on its descending / output side, the network contains one or more output neurons, which differ from neurons in that the former do not have descending neighboring / subsequent neurons.
  • the values stored in each output neuron form the output of the network.
  • the network output can, for example, be a scalar. In this case, only one output neuron will be present, and its value after the network operation will form the network output.
  • Such a network output can, for example, be a measure of the likelihood that a set of elements, that is, a set of numerical values of a presence signal, belongs to a certain class or not.
  • the network output can, however, alternatively be a vector.
  • each of the output neurons is a measure that measures the probability that the set belongs to the corresponding class associated with the corresponding component, for example, to the class of presence signals "human", “cat” and "elephant". Other examples are also possible and will be presented below.
  • a neural network includes neurons interconnected to map, in feedforward or normal operation, a set of elements to a neural output.
  • the elements of the set that is, the numerical the values of the presence signal in an exemplary case can be considered as input neurons of a network with neurons and layers formed in this case, which are intermediate neurons or intermediate layers, respectively.
  • input neurons can appropriately be considered as ascending adjacent or preceding neurons of interneurons, just as output neurons can form descending adjacent / subsequent neurons of interneurons, forming, for example, the highest intermediate layer of the network or, if one or more output neurons are interpreted as forming the topmost layer of the network, the second highest layer of the network.
  • the neural network may be implemented, for example, in the form of a computer program running on a computer or processing device, that is, in software, but implementation in hardware, for example, in the form of an electrical circuit, would also be feasible.
  • Each neuron when trained, calculates, as described above, activation based on its input values using a neural function, which, for example, is represented as a non-linear scalar function q () of a linear combination of input values.
  • neural functions associated with neurons can be parameterizable functions.
  • the neural functions for neuron j are parameterizable using an offset bj and a weight wij for all input values i of the corresponding neuron.
  • the network is, for example, reapplied to the training (training) set for element sets for which the correct network output is known, that is, the training set of labeled presence signals in the illustrative case.
  • the training (training) set for element sets for which the correct network output is known, that is, the training set of labeled presence signals in the illustrative case.
  • the embodiments described below are not limited to any source or method for determining parameters.
  • the ascending (front) part of the network consisting of layers extending from the dataset, i.e. network input, up to the intermediate hidden layer, can be artificially generated or trained to emulate the extraction of the presence signal data feature by means of convolutional filters, for example, so that each the (downstream) neuron of the subsequent layer represents the feature value from feature maps.
  • Each feature map for example, is associated with a specific characteristic or feature or impulse response or the like. Accordingly, each feature map can, for example, be viewed as a sparse (sub-) sampled filtered version of the presence input, with one feature map differing in the associated feature / characteristic / impulse response of the associated filter from the other feature map. If, for example, a set has C ⁇ elements, namely, presence signal values, that is, X columns and Y rows of signal values, each neuron will correspond to one feature value of one feature map, the value of which will correspond to a local feature estimate associated with a certain part of the signal presence.
  • N feature maps with PQ samples of feature estimates for example, P columns and Q rows of feature values
  • the number of neurons in the descending subsequent layer of the part will be equal, for example, NPQ, which may be less or more than X Y.
  • translation transformation of feature descriptions or filters underlying feature maps could be used, respectively. Note again, however, that the existence of such a "translated" rather than a “trained” portion of the network is optional for the present application and its embodiments, and that such a portion may alternatively be absent.
  • the neural function can, however, be parameterizable, and although the parameterizable neural function may be the same among these neurons, the function parameter (s) of that neural function may (may) vary among these neurons.
  • the number of intermediate layers is also arbitrary and can be one or more than one.
  • the network output will, for example, indicate that this input signal belongs to the third class, that is, to the class of signals showing a living person. More precisely, while the human output neuron would end up high, other output neurons, illustratively in this case for the cat and elephant classes, would end up at low (lower) values.
  • information as to whether or not a presence signal may be insufficient. Rather, it would be preferable to have information at the level of granularity of the presence signal values indicating which numbers, i.e. the elements of the set were relevant to the network's decision and which were not, for example, which values about the heartbeat data display a person and which do not.
  • a one-time system calibration may be required to transform the color of the spectrum detected by the photodiode array into a spatial coordinate.
  • the illumination pattern is fixed and possibly matches the photosensor filters in a 1: 1 ratio, the calibration problem is trivial.
  • Light sources can use light of invisible wavelengths, since it is not always desirable to illuminate the space with colored light. For this reason, in a preferred embodiment of the invention, it is proposed to provide a system in which the spectral bandwidth of the light source and the photosensor array filter go beyond the wavelengths of visible light, that is, 350-700 nanometers (nm).
  • IR infrared
  • UV ultraviolet
  • the spectrum frequencies used are infrared frequencies when the light is still strongly absorbed by the bloodstream in the skin.
  • This IR spectrum is well suited for both sources of infrared radiation, such as LEDs, and for photodiodes, which are readily available over the entire wavelength range of infrared radiation.
  • the invention also provides specific concepts whereby either the spectral bandwidth of the light source or the array photodiode filter is reduced.
  • the bandwidth of the light source should be within the passband of the photodiode array filter.
  • such a system may contain a discrete or continuous spectrum of light. It will be apparent to those skilled in the art that such a system can be constructed using broadband light or a series of reduced bandwidth light sources or with single frequency light sources such as lasers and LEDs.
  • each photodiode in the array has a discrete filter.
  • the described system has a self-monitoring function to determine when conditions within zones require the activation of photodetectors, and is equipped with adjusting mechanisms that can self-adjust in response to detected conditions in the area for detecting the presence of a person.
  • FPGAs programmable logic controllers
  • BMC basic matrix crystals
  • ASICs are specialized custom large integrated circuits (LSI), which are significantly more expensive for small-scale and single-piece production.
  • the FPGA itself consists of the following components:
  • Blocks can also be implemented using read-only memory devices.
  • aspects of the present technical solution may be implemented as a system, method, or computer program product. Accordingly, various aspects of the present technical solution may be implemented solely as hardware, as software (including application software, and so on), or as an embodiment combining software and hardware aspects, which may generally be referred to as a "module” , “System” or “architecture”. In addition, aspects of the present technical solution may take the form of a computer program product implemented on one or more computer-readable media having computer-readable program code that is implemented thereon. [0077] Any combination of one or more computer readable media can also be used.
  • Computer readable media storage can be, without limitation, electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus, device, or any suitable combination thereof. More specifically, examples (non-exhaustive list) of a computer-readable storage medium include: an electrical connection using one or more wires, a portable computer diskette; hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), fiber optic connection, compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any combination of the above.
  • a computer-readable storage medium can be any flexible storage medium that can contain or store a program for use by the system itself, device, apparatus, or in connection therewith.
  • Program code embedded in a computer-readable medium can be transmitted using any medium, including, without limitation, wireless, wired, fiber optic, infrared, and any other suitable network or combination of the above.
  • Computer program code for performing operations for the steps of the present technical solution may be written in any programming language or combinations of programming languages, including an object-oriented programming language such as Java, Smalltalk, C ++, and so on, and conventional procedural programming languages such as programming language "C" or similar programming languages.
  • the program code can be executed on the user's computer in whole, in part, or as a separate software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer.
  • the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN), a wide area network (WAN), or a connection to an external computer (for example, via the Internet using Internet service providers).
  • LAN local area network
  • WAN wide area network
  • Internet service providers for example, via the Internet using Internet service providers.
  • These computer program instructions may also be stored on a computer-readable medium that can control a computer other than a programmable data processing device or other devices that function in a particular way, such that the instructions stored on the computer-readable medium create a device including instructions that perform the functions / actions specified in the block diagram and / or diagram.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

La présence invention se rapporte au domaine des techniques informatiques et concerne notamment des procédés de détection de la présence d'objets avec une fonction de contrôle automatique. L'invention concerne un système de détection de la présence d'objets avec un contrôleur automatique, lequel comprend au moins une source de lumière capable d'émettre une lumière ayant un spectre prédéterminé; chacune des sources de lumière possède un spectre prédéterminé différent pour chaque zone prédéfinie; au moins un moyen sensible à la lumière d'un spectre prédéterminé capable de détecter la lumière réfléchie depuis au moins un objet si ce dernier est présent dans la zone prédéfinie, et de générer un signal de présence sur la base de la lumière détectée; et au moins un dispositif de traitement afin de déterminer la présence de l'objet sur la base du signal de présence généré lors de l'étape précédente, lequel dispositif de traitement sépare la sortie sur la présence de l'objet dans la zone prédéfinie en fonction des signaux de présence.
PCT/RU2020/000133 2020-03-13 2020-03-13 Système de détection de la présence d'objets avec un contrôleur automatique WO2021182983A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
RU2020110544 2020-03-13
RU2020110544A RU2742803C1 (ru) 2020-03-13 2020-03-13 Система обнаружения присутствия объектов с самоконтролем

Publications (1)

Publication Number Publication Date
WO2021182983A1 true WO2021182983A1 (fr) 2021-09-16

Family

ID=74554627

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/RU2020/000133 WO2021182983A1 (fr) 2020-03-13 2020-03-13 Système de détection de la présence d'objets avec un contrôleur automatique

Country Status (2)

Country Link
RU (1) RU2742803C1 (fr)
WO (1) WO2021182983A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4659922A (en) * 1985-02-19 1987-04-21 Eaton Corporation Optical sensor device for detecting the presence of an object
US8461991B2 (en) * 2009-04-09 2013-06-11 Nightwatcher Electronics (Pty) Ltd Security light with plural possible directions of illumination
US20130200254A1 (en) * 2010-10-27 2013-08-08 Koninklijke Philips Electronics N.V. A presence detection system and a lighting system
US20190324147A1 (en) * 2017-01-03 2019-10-24 Innoviz Technologies Ltd. Detecting angles of objects

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4659922A (en) * 1985-02-19 1987-04-21 Eaton Corporation Optical sensor device for detecting the presence of an object
US8461991B2 (en) * 2009-04-09 2013-06-11 Nightwatcher Electronics (Pty) Ltd Security light with plural possible directions of illumination
US20130200254A1 (en) * 2010-10-27 2013-08-08 Koninklijke Philips Electronics N.V. A presence detection system and a lighting system
US20190324147A1 (en) * 2017-01-03 2019-10-24 Innoviz Technologies Ltd. Detecting angles of objects

Also Published As

Publication number Publication date
RU2742803C1 (ru) 2021-02-10

Similar Documents

Publication Publication Date Title
Khan et al. Review of fall detection techniques: A data availability perspective
KR101571277B1 (ko) 모니터링 구역내의 개체들의 움직임을 분류하는 장치 및 방법
Fan et al. Robust unobtrusive fall detection using infrared array sensors
US20170119283A1 (en) Monitoring activities of daily living of a person
CN112232155B (zh) 非接触指纹识别的方法、装置、终端及存储介质
US20140196131A1 (en) User authentication based on a wrist vein pattern
CN108701357A (zh) 用于皮肤检测的设备、系统和方法
US20030235335A1 (en) Methods and systems for detecting and recognizing objects in a controlled wide area
US11864860B2 (en) Biometric imaging and biotelemetry system
WO2010100593A1 (fr) Procédé de gestion d'une fonction d'un dispositif, et système de détection de la présence d'un être vivant
EP1744669A1 (fr) Appareil et procede de detection de flux sanguin
Planinc et al. Robust fall detection by combining 3D data and fuzzy logic
KR102146089B1 (ko) 지문 센서를 포함하는 디바이스의 안티-스푸핑 방법 및 시스템
CN111524608A (zh) 智能检测与防疫系统和方法
CN114088644A (zh) 感测装置、感测方法及感测操作组件
CN110337656A (zh) 用于人脸识别的光学传感器、装置、方法和电子设备
Matey et al. Iris recognition in less constrained environments
Mondol et al. Hawad: Hand washing detection using wrist wearable inertial sensors
WO2020227986A1 (fr) Appareil et procédé de collecte d'images, et dispositif électronique
RU2742803C1 (ru) Система обнаружения присутствия объектов с самоконтролем
Fan et al. Fall detection with unobtrusive infrared array sensors
WO1996038820A1 (fr) Systeme de regulation securitaire
WO2022100981A1 (fr) Système de contrôle d'accès pour personnes autorisées et sans symptômes
CN210515292U (zh) 基于飞行时间法3d建模技术防控儿童近视的系统
Logu et al. Real‐Time Mild and Moderate COVID‐19 Human Body Temperature Detection Using Artificial Intelligence

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20924310

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20924310

Country of ref document: EP

Kind code of ref document: A1