WO2018058705A1 - 基于视频精神生理参数监测危险人物装置及方法 - Google Patents

基于视频精神生理参数监测危险人物装置及方法 Download PDF

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Publication number
WO2018058705A1
WO2018058705A1 PCT/CN2016/102065 CN2016102065W WO2018058705A1 WO 2018058705 A1 WO2018058705 A1 WO 2018058705A1 CN 2016102065 W CN2016102065 W CN 2016102065W WO 2018058705 A1 WO2018058705 A1 WO 2018058705A1
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video
frequency
image
vibration
danger
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PCT/CN2016/102065
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English (en)
French (fr)
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金湘范
崔晋官
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金湘范
崔晋官
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1101—Detecting tremor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames

Definitions

  • the invention relates to a device and a method for monitoring dangerous people based on video psychophysiological parameters.
  • the patent document of CN201200409 discloses a lie detection system with visual stimulation detection function, which comprises a main control component, a receiving terminal and a lie detector, and the receiving terminal and the lie detector are respectively connected with the main control component, and the measured person is placed.
  • the person to be tested wears a sensor for lie detection, and the sensor is connected to the lie detector.
  • the lie detection system further includes at least one camera connected to the main control unit by a video capture device for monitoring the behavior of the person being tested.
  • the utility model induces the related psychological and physiological responses by presenting the visual stimuli associated with the case to the test subject, and diagnoses the relationship between the tested person and the case by measuring and analyzing the relevant psychophysiological reaction, and captures the measured human eye by means of the camera.
  • This contact and other contact sensor uses techniques that do not allow the subject to experiment without knowing it. Subjects exposed to contact psychophysiological tests are known to have aggravated the results of the test analysis. The reason is that the subject always wants to cover up some information to prepare. Good people who have not done bad things will have constant pressure and anxiety during the test.
  • the contact method uses a modern stereo pulse measurement system based on the fingertips of the human body. Tens of thousands of pulse pulses are generated to measure changes in psychophysiological signals produced in the human body. Physiological signals are detected in a variety of ways by contact sensors.
  • the prior art has appeared in a non-contact method to detect real-time acquisition in the case where a subject (human) is not found, and information on a state of non-contact human psychophysiological characteristics.
  • the operability is not strong, and different state changes caused by the acquisition of information are not reflected very accurately.
  • the object of the present invention is to overcome the above-mentioned deficiencies and to provide a device for monitoring dangerous persons based on video psychophysiological parameters, which is highly maneuverable and can easily measure each parameter of a subject's psychophysiological characteristics to detect a potentially dangerous or suspicious person.
  • the technical solution adopted by the present invention is: a device for monitoring a dangerous person based on a video mental physiological parameter, comprising: an acquiring unit, configured to acquire video information of a test subject; and a generating unit, configured to perform the video information Processing to generate a biological signal related to the degree of danger; a processing unit for obtaining a degree of danger of the test subject according to the vibration frequency, and/or amplitude of the biological signal.
  • Another object of the present invention is to provide a method for monitoring a dangerous person based on a video psychophysiological parameter, comprising: acquiring video information of a test subject; and processing the video information to generate a biological signal related to a degree of danger According to the vibration frequency, and/or amplitude of the biological signal, the degree of danger of the person to be tested is obtained.
  • the implementation is simple, according to the biological signal acquisition method, by capturing the video image information process of the subject; using the above video stream information to analyze the process of extracting the vibration parameter of the subject object; and then generating each parameter process of the psychophysiological characteristic based on the vibration parameter
  • the signal extraction process for the physiological characteristics of the subject is started with each of the above parameters.
  • control is convenient, and the device is operated in a very simple manner by centrally managing and detecting dangerous people (suspicious people) by constructing multiple IP-type CCTVs (network cameras) and network connection server systems.
  • IP-type CCTVs network cameras
  • network connection server systems In order to obtain the target object information, various suggested methods of application equipment with feasible and effective effects are provided.
  • Fig. 1 is a flow chart showing how the biosignal is acquired according to an embodiment of the invention.
  • FIG. 2 is a block diagram showing the functional classification of the electronic device implemented in FIG.
  • FIG. 3a is a structural diagram of an electronic device implemented in FIG. 1.
  • FIG. 3a is a structural diagram of an electronic device implemented in FIG. 1.
  • Figure 3b is a block diagram associated between the elements of the electronic device of Figure 3.
  • FIG. 4 is a flow chart of another embodiment of the invention, according to a biosignal acquisition method.
  • Fig. 5a is a diagram showing the amplitude of the vibrating picture as part of forming a human body image, surrounded by bioenergy radiation.
  • Figure 5b is a graphical representation of the bioenergy radiation around the actual video stream of the human body.
  • FIGS. 6a and 6b are diagrams showing the emission of biological images according to the state of the subject, Fig. 6a is a steady state, and Fig. 6b is a state of restlessness and pressure.
  • Fig. 7a is a distribution chart of a human body vibration image frequency generating element (biological signal image) in a steady state.
  • Fig. 7b is a distribution chart of the human body vibration image frequency claim element (biological signal image) under pressure.
  • FIG. 8 is a structural schematic diagram of a device for monitoring dangerous people based on video psychophysiological parameters according to the present invention.
  • Biosignal detection device 31 Camera 32: A/D converter 33: Memory
  • the device for monitoring a dangerous person based on the video mental physiological parameter of the present invention includes: an obtaining unit 100, configured to acquire video information of the test subject; and a generating unit 200, configured to process the video information, To generate a biological signal related to the degree of danger; the processing unit 300 is configured to obtain the degree of danger of the tested person according to the vibration frequency, and/or the amplitude of the biological signal.
  • the invention includes the process of capturing continuous dynamic image information of a subject by a camera according to a biological signal acquisition manner; analyzing a process of extracting a vibration parameter of a subject object by using the above video stream information; and then generating each parameter process of psychophysiological characteristics based on the vibration parameter;
  • the signal extraction process for the physiological characteristics of the subject is initiated with each of the above parameters; all processes including the above.
  • the above physiological characteristic signal corresponds to a physiological characteristic signal of the subject to generate a visualization process of the image; more includes the above processes.
  • the invention is based on a physiological signal detecting device: a portion obtained by capturing a continuous moving image video stream acquired by a subject object; the above continuous video capturing light detecting portion, and a video data conversion A/D of the captured video stream (Analogue->Digital)
  • the conversion portion the converted continuous video data to analyze the vibration parameters for measurement, the measurement based vibration parameter biosignal image generation process, and the above generated biosignal image representation of the identification portion included in the manner of providing a camera built-in or Externally connected to the device to achieve.
  • the invention is seen in Figure 2, the changes in the psychophysiological properties produced by the organism, such as photographing the subject continuously producing a varying video stream (11), processing each parameter (12) generated by the video stream (analysis), Extracting the physiological signal (13) by each parameter, obtaining the physiological signal by the above method can be used for various purposes (14), and the text or video stream morphology for evaluating the emotional state of the subject prompts the plurality of subjects corresponding to the above physiological signals.
  • the status of the video stream content mode prompt is implemented as the structural device shown in Figure 2.
  • 2 is a functional block diagram of a physiological signal detecting device. Referring to FIG.
  • the physiological signal detecting device is photographed by the subject (or the subject 1) camera (21), Video stream processing (22) from camera (21) to acquisition video stream analysis, from video stream processing (22) to extraction of vibration parameters by signal (each parameter), signal analysis section generated by using physiological signals as above (23)
  • the application portion (24) for the physiological signal application acquired by the signal analysis portion (23) is included, and the application portion (24) here is, for example, a display screen of a text or video stream representation generated by the above physiological signal is included.
  • the degree to which each parameter of the vibration is applied is included.
  • ⁇ Planck constant photon energy and frequency
  • obtaining the frequency component of the biosignal image i.e., the frequency of vibration (position change, fluctuation) generated at each part
  • Biosignal image analysis can also be performed by humans or mathematically by digital biosignal images and specific elements processed by programs. Mathematical processing algorithms are most effective in creating and analyzing biosignal imagery that monitors displays like color video streams or visual analysis.
  • the frequency component of the biosignal image that needs to be derived is the human psychophysiological characteristic state and the continuous emotional state level. It can be seen at a glance that the human body state changes caused by various human stimuli are classified.
  • the amplitude modulation of the vibration image is the amplitude of the position change frequency or the average value of the amplitude generated by the specific region of the human body at the maximum vibration frequency of the target, and any change in the physiological and physiological characteristics of the human body displayed by the modulating is clear at a glance. Record instantly.
  • the fractal dimension of the brain is the most important way to achieve understanding in the process of learning, memory and solving various problems. According to the experiment, the most concentrated part of the human body vibration is the brain.
  • the frequency component of the vibration image is that the image existing around the head of the person is more vibrating than the surrounding body.
  • the image shows a bigger one.
  • the changes produced by the human body are displayed in such a way that the vibration image is uneven or the color form is asymmetrical. This can be understood by looking at the biosignal image.
  • the most abundant signal is the frequency average level of the maximum vibration frequency transmission of the human emotion state or the background level blur between adjacent points or the real change concealment generated when the biosignal image is visually received.
  • An amplitude component that is different from the components of the frequency is more effective than the geometry. The most important is that the geometrically connected biosignal image amplitude of the vibration point constitutes the quality assessment of the desired biosignal image and the more precise determination of the parameters for system adjustment.
  • the parameters of the potentially dangerous state (or suspected state) of the psychophysiological characteristics indicate that the aggressiveness, stress and anxiety of the negative emotional parameters are the most influential parameters.
  • the formula for detecting the “degree of danger (suspiciousness)” of potentially dangerous persons or suspicious persons of this design is as follows. In the case of a subject video stream acquisition of an image frame, a large image of the subject is taken from the crowd as much as possible.
  • the embodiment provides a method for monitoring a dangerous person based on a video psychophysiological parameter, including: step S100, acquiring video information of a test subject; and step S200, processing the video information to generate a risk degree correlation a biosignal; step S300, obtaining a degree of danger of the test subject based on the vibration frequency, and/or amplitude of the biosignal.
  • the degree of danger of obtaining the person being tested is based on the following algorithm:
  • the aggression, stress, anxiety values of the above negative emotions are compared with the general average of each parameter, and each condition has a different risk level (suspicious level) as exemplified below.
  • the following three scenarios of the risk (suspiciousness) level of 80 or more are obtained.
  • the conditions for the level of danger (suspiciousness) reaching 100 are as follows. There are four types of formulas.
  • the embodiment provides a method for monitoring a dangerous person based on a video psychophysiological parameter, comprising: step S100, acquiring video information of a test subject; and step S200, processing the video information to generate a biological signal related to a degree of danger; S300: Obtain a danger degree of the test subject according to the vibration frequency, and/or amplitude of the biosignal.
  • the degree of danger of obtaining the person being tested is based on the following algorithm:
  • N-50 frames have high limits, and the difference between the frames is statistically calculated.
  • the following is a vibration image acquisition and a statistically deterministic way of vibrating image information for the subsequent aggressiveness level to find each parameter, which is determined by symmetrical each parameter with the vibration of any amplitude and frequency vibration image.
  • the level of aggression determines that, in contrast to the opposite approximation method we know, the human head scans the new formula of the motion amplitude and frequency symmetry of the individual thermal forces. This is the maximum symmetry of the vibration and fine motion of the amplitude and frequency vibration image processing between 20 seconds in the case of a person with a maximum level of aggressiveness. At the same time, the level of stress and anxiety will be low.
  • the embodiment provides a method for monitoring a dangerous person based on a video psychophysiological parameter, comprising: step S100, acquiring video information of a test subject; and step S200, processing the video information to generate a biological signal related to a degree of danger; S300: Obtain a danger degree of the test subject according to the vibration frequency, and/or amplitude of the biosignal.
  • the degree of danger of obtaining the person being tested is based on the following algorithm:
  • N—target has the largest calorific value
  • the level of anxiety is decisive and the existing approximation method is known to be different.
  • the new formula provided by the high anxiety and low frequency spectral density compared to the actual situation of the spectral density enhancement of the exercise frequency is as follows.
  • the embodiment provides a method for monitoring a dangerous person based on a video psychophysiological parameter, comprising: step S100, acquiring video information of a test subject; and step S200, processing the video information to generate a biological signal related to a degree of danger; S300: Obtain a danger degree of the test subject according to the vibration frequency, and/or amplitude of the biosignal.
  • the degree of danger of obtaining the person being tested is based on the following algorithm:
  • the invention is not only provided by the state of the emotional and psychophysiological properties of the human being to determine the application.
  • the state characteristics of the reference person are classified by more than 200 of the multiple types of systems.
  • each parameter can describe all states of the person.
  • the statistics of the authenticity of psychology in the traditional concept of motion related to the head of each person The reflection of subtle motion transformation is understood by the principle of ambiguity.
  • Each parameter is usable.
  • the micro-dynamic quantitative analysis of head reflex can make the human psychophysiological state test more objective and scientific, and can solve many problems in medicine, psychology, psychiatry and daily life.
  • the expert professional evaluation degree of the invention is (90% or more), and the invention is reconfirmed.
  • the functionally modular device illustrated in FIG. 2 is a method (21) that can perform the method of the present invention as described above, and includes an imaging element such as a CCD or a CMOS and an A/D converter for digitizing the analog signal.
  • the image processing unit (22) is content such as an encoder that generates a video in a specific format.
  • the signal analysis unit (23) measures vibration waves using the above-described image according to the above-described method, and generates or screens psychophysiological information (signal or biosignal) therefrom.
  • the vibration wave includes the vibration frequency, amplitude, and phase of the position change of the different parts of the detected person.
  • the psychobiology information can include psychological, emotional, and emotional states such as steady state, excited state, and stress state.
  • the biosignal application section (14) is a biosignal processing algorithm that estimates the mental and emotional state of the subject using the above biosignal, and displays the result thereof.
  • biosignal processing algorithms include algorithms for detecting dangerous people or suspicious people.
  • the display prepared in the above application section (24) is as described above, and the final result is displayed in the form of a text or an image. It can be embodied by the various types of system devices described above. For example, a personal computer that mounts a camera, a portable terminal that mounts a camera, a tablet PC based on Windows mobile, Android, IOS, Symbian, BlackBerry, BADA, etc., a tablet, a PDA, a smart phone, and thus can be run in an internal application. OS digital cameras are a typical example.
  • the biosignal detecting device (30) is a camera sub-image capturing portion, that is, a lens (31), a slave camera (31), a camera (32).
  • the A/D converter (32) for digitizing the analog video signal
  • the processor (34) for performing image analysis and parameter screening, and the biosignal generation
  • the display portion (35) for displaying the result.
  • an input device for inputting foreign information, such as a key input portion (36) such as a numeric keypad, and a memory-containing storage portion (33) used in the above-described video signal processing or the like.
  • Fig. 3b is a schematic view showing a schematic configuration of the apparatus of Fig. 2, illustrating an interaction relationship between constituent elements.
  • reference numeral 1 is an electronic block (wireless signal receiver) and transmitter and audio/video signal, signal-audio/video conversion, and the like.
  • reference numeral 42 a digital camera including a video camera element, an objective lens, and the like is acquired from a test subject (testee or test subject).
  • Reference numeral 43 is required for the full use of the smartphone, especially for the memory of the digital image storage.
  • Reference numeral 44 is a display device for visualizing text and picture forms such as a user interface and processing results.
  • Reference numeral 45 The control and synchronization processor for each of the above electronic blocks, digital cameras, memories, displays, and the like.
  • reference numeral 46 is a main body including a numeric keypad, such as a power source required for the entire system drive and a user's input.
  • FIG. 4 is a flow chart of a camera acquisition process based on a biosignal video in accordance with the present invention, which may be utilized by the apparatus of FIG. 3a, specifically FIG. 3b.
  • an image of a subject is acquired from a camera (31) and converted into a pre-simulation signal (S10, S20).
  • the signal obtained from the image of the subject is the previous analog signal, and thus is converted into digital video data (S30) by the A/D converter (inverter) (32).
  • the processor (34) analyzes the vibration parameters for each image data as a function of time (S40).
  • the vibration parameter includes at least one of a vibration frequency, an amplitude, and a phase according to a change in position of a different portion of the test subject. That is, the processor (34) analyzes the positional change of each part of the test subject, and analyzes the vibration frequency of each part, the magnitude of the position change (the magnitude of the vibration), and the phase calculation.
  • the processor (34) analyzes the difference between the images using the vibration image analysis program, measures the change in the position center of gravity, or calculates the vibration parameter using the Fourier transform. The calculation of the vibration parameters is described in detail below.
  • the processor (34) separates the contours uniformly (left, right) from a continuous plurality of videos after confirming according to the contour change or vibration dynamics of the subject. Secondly, the location where the maximum vibration frequency is generated is determined in the two laterally divided portions. This frequency determines the corresponding horizontal horizontal color of the biosignal image.
  • the processor (34) generates a biosignal image based on the calculated vibration parameters. (S50).
  • the biosignal image may include an amplitude component and a frequency component.
  • the amplitude component is referred to as "internal biosignal image”
  • the frequency component is referred to as "biosignal image”.
  • the concepts and definitions of such terms are Learn from the description in Figure 5.
  • the processor (34) acquires the psychophysiological information of the subject by the calculated vibration parameter (S60). That is, the processor (34) can analyze the mental state of the object (20) by analyzing the vibration parameters.
  • Fig. 5a is a diagram showing biological energy and gas field radiation generated around a human body image formed by an amplitude component of a vibration image.
  • the internal biosignal image is expressed by the color according to the positional change of each part. The size of the position change of the subject (1) is visualized by these.
  • the external biological image appears around the internal biosignal image, and the average maximum vibration frequency is represented by the color transition.
  • Figure 5b is an image of a radioactive bioenergy biosignal around the actual image of the human body. In Figure 5b, there is no representation of the internal biosignal image showing only the biosignal image around the actual image.
  • Figures 6a, 6b are biosignal images each representing a steady state and an unstable state
  • Fig. 6a is a steady or normal state
  • Fig. 6b is a biosignal image of the object under test in a pressure state.
  • the biosignal image is completely symmetrical from the form and color, and the color of the biosignal image is displayed as the medium color of the selected color scale (whole color-green).
  • the gas field on the biosignal image contains many red components. Therefore, in this state, it can be known that the object under test is in an unstable state.
  • Fig. 7a is a distribution diagram of frequency constituent factors (biological signal images) of a human body vibration image in a steady state
  • Fig. 7b is a distribution diagram of frequency constituent factors (biological signal images) of a human body vibration image in a state of pressure.
  • the graph shown in Figure 6a is a typical frequency distribution of a person under normal operating conditions. Studies have shown that most people in the steady state, there will be a distribution value similar to the single pattern distribution law.
  • the situation of the testee becomes as shown in Figure 6b. If the average (middle) of the frequency distribution (M) in fear, stress, and aggressive states moves in the increasing direction. The (M) average (intermediate) value of the frequency distribution value in the steady state is shifted in the decreasing direction.
  • the frequency axis (X) can be expressed not only in relative units but also in actual units or time (Hz or sec.). The distance between the displayed values is determined by the actual parameters of the camera's rapid processing and the software settings (processing program, image storage and image time).

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Abstract

一种基于视频精神生理参数监测危险人物装置及方法，该装置包括：获取单元（100），用于获取被测试人视频信息；生成单元（200），用于对所述视频信息进行处理，以生成包含危险程度相关的生物信号；处理单元（300），用于根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。本装置和方法的优点是：实现简单，根据生物信号获取方式，通过捕捉受试者视频图像信息过程；用以上视频流信息分析受试者对象提取振动参数过程；然后基于振动参数生成精神生理学特性的每个参数过程；用以上每个参数开始对受试者生理学特性提取信号过程。

Description

基于视频精神生理参数监测危险人物装置及方法 技术领域
本发明涉及一种基于视频精神生理参数监测危险人物装置及方法。
背景技术
CN201200409的专利文献公开了一种带有视觉刺激检测功能的测谎系统，它包括主控部件、接收终端和测谎仪，接收终端、测谎仪分别与主控部件相连，被测人被置于接收终端的前方，被测人的身上佩戴用于测谎的传感器，该传感器与测谎仪相连。测谎系统还包括至少一个摄像头，该摄像头通过视频采集设备与所述主控部件相连，该摄像头用于监控被测人的行为。本实用新型通过向被测人呈现与案件相关的视觉刺激来诱导其相关心理生理反应，通过测量分析相关心理生理反应诊断出被测人与案件间的关系，并且借助摄像头捕获被测人眼部变化来防止被测人通过转移视线而对视觉刺激的躲避。这种接触式和有其他问题的接触式传感器使用的技术无法让受试者不知情的情况进行实验。接触式精神生理学测试的受试者已知的情况下会加重测试分析的结果产生很多困难。原因是受试者总想着掩饰一些信息做准备，没有做过坏事的好人在测试过程中会有持续的压力和焦虑感，接触式方法中使用现代的立体脉搏测量系统，基于人体手指指尖产生的数万次的脉搏脉冲测量人体内产生的精神生理学信号变化。通过接触式传感器用多种方式使用生理学信号进行检测。以上方法分析人体状态的工作一般需要几个小时的准备给受试者身上牢牢安装传感器，并且需要专业性非常强的熟练操作人员共同参与其中，所以人体精神生理学诊断需要准备以上的系统，为广泛的使用该系统上实际上会有许多约束条件。
另外，现有技术虽然出现了非接触式方法检测受试者对象(人)不被发现的情况下实时的获取，通过非接触式人体精神生理学特性状态的信息。但是可操作性不强，获取信息产生的不同的状态变化不会非常准确的反映出来。这说明分析的图片(之间)差异状态无法准确的反映出对象位置变化之间的差异相关联。即空间内位置的细微变化分析目标对象非常困难，对于目标对象精神生理学特性每个参数测量准确度和可靠性会有所下降。
发明内容
本发明的目的在于克服上述不足，提供一种基于视频精神生理参数监测危险人物装置，其操控性强，能够轻易对受试者精神生理学特性的每个参数测量来检测潜在危险或可疑人员。
为了实现上述目的，本发明采用的技术方案为：一种基于视频精神生理参数监测危险人物装置，包括：获取单元，用于获取被测试人视频信息；生成单元，用于对所述视频信息进行处理，以生成包含危险程度相关的生物信号；处理单元，用于根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
本发明的另一目的在于提供一种基于视频精神生理参数监测危险人物方法，其特征在于，包括：获取被测试人视频信息；对所述视频信息进行处理，以生成包含危险程度相关的生物信号；根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
本发明的还一目的在于提供一种基于视频精神生理参数监测危险人物方法，其特征在于，包括：获取被测试人连续产生变化的视频流信息；对所述视频流信息进行处理，以生成包含危险程度相关的生物信号；根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
本发明的有益效果为：
第一，实现简单，根据生物信号获取方式，通过捕捉受试者视频图像信息过程；用以上视频流信息分析受试者对象提取振动参数过程；然后基于振动参数生成精神生理学特性的每个参数过程；用以上每个参数开始对受试者生理学特性提取信号过程。
第二，操控方便，设备通过搭建多台IP型CCTV(网络摄像机)和网络连接服务器系统分析的中央集中化管理检测危险人物(可疑人物)的非常简单的方式操作使用。为获取目标对象信息，提供多种可行性和效果明显的应用设备的建议方法。
第三，应用广泛，相对的非接触式方式使受试者在未知的情况下会放松警惕不会太过于掩饰，对于了解人体状态的非接触式检测会让受试者放松警惕。可以避免人体状态分析诊断过程产生的多种问题和误差，在精神医疗、 心理学领域应用非常广泛。
附图说明
此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：
图1为该发明的实施案例，根据生物信号怎样获取的流程图。
图2为在图1中实现的电子设备功能分类概括框图。
图3a为在图1中实现的电子设备结构配置图。
图3b为在图3中电子设备构成的要素间相关联的框图。
图4是该发明的其他实施案例，根据生物信号获取方法流程图。
图5a是振动图片振幅作为一部分形成受试者人体图像的，周围环绕生物能量放射的图示。
图5b是人体实际视频流周围生物能量放射图示。
图6a、6b是根据受试者状态显示生物图像放射的图示、图6a为稳定状态，图6b为不安、压力状态。
图7a是稳定状态的人体振动图像频率生成要素(生物信号图像)的分布图表。
图7b是压力状态下人体振动图像频率声称要素(生物信号图像)的分布图表。
图8为本发明的基于视频精神生理参数监测危险人物装置的结构原理图。
各附图标记示意：
30：生物信号检测装置 31：摄像头 32：A/D转换器 33：储存器
34：处理器 35：显示部位 36：钥匙入口
具体实施方式
如在说明书及权利要求当中使用了某些词汇来指称特定组件。本领域技术人员应可理解，硬件制造商可能会用不同名词来称呼同一个组件。本说明书及权利要求并不以名称的差异来作为区分组件的方式，而是以组件在功能 上的差异来作为区分的准则。如在通篇说明书及权利要求当中所提及的“包含”为一开放式用语，故应解释成“包含但不限定于”。“大致”是指在可接收的误差范围内，本领域技术人员能够在一定误差范围内解决所述技术问题，基本达到所述技术效果。说明书后续描述为实施本申请的较佳实施方式，然所述描述乃以说明本申请的一般原则为目的，并非用以限定本申请的范围。本申请的保护范围当视所附权利要求所界定者为准。
实施例1
请参照图1－图8，本发明的基于视频精神生理参数监测危险人物装置，包括：获取单元100，用于获取被测试人视频信息；生成单元200，用于对所述视频信息进行处理，以生成包含危险程度相关的生物信号；处理单元300，用于根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
包括该发明根据生物信号获取方式，通过摄像机捕捉受试者连续动态图像信息过程；用以上视频流信息分析受试者对象提取振动参数过程；然后基于振动参数生成精神生理学特性的每个参数过程；用以上每个参数开始对受试者生理学特性提取信号过程；包括以上在内所有过程。根据该发明的实施案例，以上生理学特性信号相应的受试者生理学特性信号来生成图像的可视化过程；更包括以上所有在内的过程。
该发明根据生理信号检测设备：是对受试者对象拍摄获取的连续动态图像视频流获取的部分；以上连续视频拍摄光检测部分、拍摄视频流的视频数据转换A/D(Analogue—＞Digital)的转换部分、转换的连续视频数据来分析振动参数进行测量、基于测量的震动参数生物信号图像生成过程及以上生成的生物信号图像表示的标识部分包括在内的特征的方式，提供摄像机内建或外部连接该设备来实现。
该发明在图2中看到的，生物产生的精神生理学特性变化，例如拍摄受试者连续产生变化的视频流(11)、按照视频流处理(分析)振动产生的每个参数(12)、通过每个参数提取生理信号(13)、通过以上方式获取生理信号可用于多种用途(14)、评估受试者情感状态的文字或视频流形态提示以上生理信号相对应的受试者多种状态的视频流内容方式提示。这种方式如图2中显示的结构设备来实现。图2是生理信号检测设备功能框图。参考图2根据该发明生理信号检测设备是受试者(或被拍摄者1)摄像机(21)拍摄、 从摄像机(21)到获取视频流分析的视频流处理(22)、从视频流处理(22)到通过信号提取振动参数(每个参数)、按以上利用生理信号生成的信号分析部分(23)、对于信号分析部分(23)获取的生理信号应用的应用部分(24)包括在内、这里的应用部分(24)是，比如以上生理信号产生的文字或视频流表示的显示画面包括在内。以上或相同的视频处理部分(22)及应用部分(24)的CPU(Central Processing Unit)或基于应用程序(AP)的应用程序等构成。对于该发明技术范围内的应用类型没有任何限制，所以利用振动的每个参数怎样去应用的程度包括在内非常明确。
本发明的技术原理是：粒子是物理学的物质波动特性和粒子特性之间没有明确的界限，光子能量(ε)是通过已知的普朗克常量的光子能源和频率(ν)相连接的(ε＝hν)。生物体和各部位放射的能量空间内，该部位振动频率和这一比例的假说。通过结论来说在记录生物体产生出的能量，需要记录生物体多个部位产生的振动(空间内或每部位之间)。这个过程需要保证有效分辨率和快速处理能力的非接触式TV系统来实现。此外，获取生物信号图像频率构成要素(即每个部位产生的振动(位置变化，波动)频率)是被观察的生物能量，即拥有精神生理学特性的庞大的信息。生物信号图像分析也可由人来去实现或通过数字生物信号图像和特定要素用程序处理的数学方式进行处理。数学处理的算法创建和分析的监控显示器的类似彩色视频流或可视化分析的生物信号图像方式最为有效。换句话说，需要得出的生物信号图像频率构成要素是人体精神生理学特性状态和持续的情感状态水平一目了然的看出人的多种刺激产生的人体状态变化进行分类。所有思考和行为或任何情况下关于反应情感状态是瞬间产生的变化(每个生物信号图像)是连续的，所以生物信号图像的信息数(摄像机分辨率)和可快速处理的系统之间能够找出最优的关系是非常重要的。振动图像增加的大小振幅调整(amplitude modulating)是对目标最大振动频率的人体特定区域产生的位置变化频率数或振幅平均值，通过颜色调整(modulating)显示出的人体精神生理学特性的任何变化一目了然的瞬间进行记录。大脑维度变化(fractal fluctuation)是学习、记忆及多种课题的解决过程中了解的有着最为核心作用的实现方式。根据实验显示人体振动最为集中产生的部位为大脑，大部分情况振动图像的频率构成要素是人的头部周围所存在图像要比身体周围振动 图像显示更大。人体产生的变化是振动图像不均匀或颜色形态不对称的方式显示。这是可以通过看生物信号图像来了解。根据实验结果包含最多的信号是人体情感状态的最大振动频率传递的频率平均水平或相邻点之间的背景水平模糊或生物信号图像的可视化接收时产生的真实变化隐蔽的情况发生。与频率构成的要素不同的振幅构成要素，要比几何学相关联的更为有效。最重要的是振动点位的几何学连接的生物信号图像振幅构成要的生物信号图像的品质评估和为系统调整的更为精确的确定参数建立。
精神生理学特性上人的潜在危险状态(或是有嫌疑的状态)的参数表示负面情绪参数的攻击性、压力和焦虑的是影响最大的参数。此设计方案的潜在危险人物或可疑人物检测“危险程度(可疑程度)”标识的公式如下。图像帧的受试者视频流获取情况下从人群中尽可能拍摄受试者的大图像。
请参照图9，本实施例提供一种基于视频精神生理参数监测危险人物方法，包括：步骤S100，获取被测试人视频信息；步骤S200，对所述视频信息进行处理，以生成包含危险程度相关的生物信号；步骤S300，根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
所述得到被测试人的危险程度基于以下算法：
Figure PCTCN2016102065-appb-000001
变量说明如下：
Dn－危险状态(可疑状态)水平
Ag－攻击性水平
St－压力水平
Tn－焦虑感水平
N－参数的一般平均值
以上负面情绪的攻击性、压力、焦虑感值和每个参数的一般平均值进行比较，每个条件会有不同的危险度水平(可疑度水平)为如下举例。危险度(可疑度)水平达到80以上条件的如下3种情节得出。
(攻击性远比一般值要高，但是压力和焦虑感低的情况下：)
Figure PCTCN2016102065-appb-000002
 (攻击性和焦虑感远比一般值低，但是压力值高的情况下：)
Figure PCTCN2016102065-appb-000003
(攻击性和压力感远比一般值低，但是焦虑值高的情况下：)
Figure PCTCN2016102065-appb-000004
危险程度(可疑度)水平达到100的条件如下有4种类型的公式得出。
(攻击性和压力感远比一般值高，但是焦虑值低的情况)
(攻击性和焦虑感远比一般值高，但是压力值低的情况)
(攻击性远比一般值要低，但是压力和焦虑感高的情况)
(攻击性和压力值和焦虑感值都比一般值还要高的情况)
Figure PCTCN2016102065-appb-000005
实施例2
本实施例提供一种基于视频精神生理参数监测危险人物方法，包括：步骤S100，获取被测试人视频信息；步骤S200，对所述视频信息进行处理，以生成包含危险程度相关的生物信号；步骤S300，根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
所述得到被测试人的危险程度基于以下算法：
Figure PCTCN2016102065-appb-000006
FM－频率分布密度直方图的最大频率
Fi－没时间段50帧来获取频率分布密度的直方图i的频率数的统计计算数
Fin－振动图像处理频率
n－50帧还有高的限制值得帧之间的差异统计计算数
这样的方程式对所有这些攻击性水平进行测定，自然地更低的攻击性状态接近0的水平。高攻击性状态的人的数值接近1数值。为找到发现潜在危 险的人，需要振动图像系统的安全系统应用时，对这些攻击性极限值达到0.75来使用。
以下是振动图像获取和为之后的攻击性水平决定性意义的振动图像信息每个参数统计方式来找寻，这是比任何振幅和频率振动图像的振动对称每个参数来做决定。
攻击性水平决定的是，以我们所了解的相反的逼近法不同，人的头部扫描个别热力的运动振幅及频率对称考虑的全新的公式。这是攻击性水平最大值的人的情况下20秒之间的振幅及频率振动图像处理的振动和细微运动的最大对称特性。同时压力和焦虑的水平会很低。
实施例3
本实施例提供一种基于视频精神生理参数监测危险人物方法，包括：步骤S100，获取被测试人视频信息；步骤S200，对所述视频信息进行处理，以生成包含危险程度相关的生物信号；步骤S300，根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
所述得到被测试人的危险程度基于以下算法：
Figure PCTCN2016102065-appb-000007
Figure PCTCN2016102065-appb-000008
目标左侧部分的“I”热振动图像总振幅
Figure PCTCN2016102065-appb-000009
目标右侧部分的“I”热振动图像总振幅
Figure PCTCN2016102065-appb-000010
开始到
Figure PCTCN2016102065-appb-000011
之间的最大值
Figure PCTCN2016102065-appb-000012
目标左侧部分的“I”振动图像最大频率
Figure PCTCN2016102065-appb-000013
目标右侧部分的“I”热振动图像的最大频率
Figure PCTCN2016102065-appb-000014
开始到
Figure PCTCN2016102065-appb-000015
之间的最大值
n–目标占最大的热值
之前提供的信息统计每个参数和类似的提供的公式是0到1为止压力水 平(St)测量方式。这种不管怎样最小压力水平需要符合最小测定值，且高水平的压力状态的人情况下压力数值接近1的百份比率。
以下是振动图像获取和之后决定性的焦虑感水平非常有意义的振动图像信息每个参数统计方式寻找的方式。这种方式远比振幅及频率振动图像的快速运动信号的频率光谱构成有关。
焦虑感水平是决定性的已经所知的现有逼近法不同，高焦虑感和低频率光谱密度要比运动频率搞得光谱密度增强的实际情况考虑在内提供的全新的公式如下。
实施例4
本实施例提供一种基于视频精神生理参数监测危险人物方法，包括：步骤S100，获取被测试人视频信息；步骤S200，对所述视频信息进行处理，以生成包含危险程度相关的生物信号；步骤S300，根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
所述得到被测试人的危险程度基于以下算法：
Figure PCTCN2016102065-appb-000016
Tn–焦虑感水平
Pi(f)–振动图像频率扩散动力光谱
fmax–振动图像频率扩散光谱最大频率
之前提供的信息统计每个参数和类似的公式是0到1为止的焦虑感水平测量方式。再则最小水平的焦虑感最小测定值符合的焦虑感水平高的压力值接近1的百份比率。
振动图像快速信号频率扩散光谱的操作或系统操作人员的控制方式体现。
以上了解该发明不仅仅提供的人的情感及精神生理学特性状态来测定实例来应用。参考人的状态特性是多种类型系统的200个多个以上进行分类的。对于该发明的头部细微振动每个参数或头部振动图像每个参数可描述人的所有状态。心理学在运动相关的传统概念的真实性的统计每个参数的人的头部 反射细微运动转换是不明确的原则来理解。但是对于以上提供的逼近法为基础的技术信息系统和类似的人的状态可以明确确定人的状态特征的信息每个参数都是可使用的。
头部反射的微动态量化分析可让人类精神生理学状态测试更加客观、科学，能够解决许多医学、心理学、精神医学和日常生活中的问题。
根据攻击性、压力、不安、潜在危险的机场内乘客精神状态的量化评价及开发的系统和相关的独立实验，对本发明的专家专业评价认可度为(90％以上)，也再次确认了本发明的实际实现可能性。
功能性模块化的图2上图示的装置是，可执行同上述的本发明的方法摄像机(21)是，包括CCD或CMOS等摄像元件以及由此的模拟信号数字化的A/D转换器，图像处理部(22)是包括特定格式的生成视频的编码器等内容。
信号分析部(23)是利用上述影像按照前述的方法测定振动波并从此生成或筛选精神生理学信息(信号或生物信号)。在此振动波包括着被检测人的不同部位的位置变化的振动频率、振幅和相位等内容。并且精神生物学信息是可以包含稳定状态，兴奋状态，压力状态等心理/情感/感性状态。另一方面，生物信号应用部分(14)是包含着利用上述生物信号评估被检测人的精神、情绪状态的生物信号处理算法及其结果的显示等。例如，生物信号处理算法是包含着检测危险人物或可疑人物的算法。在上述应用部分(24)准备的显示如前所述，最终得到的结果显示文字或图像的形式。可通过上述的各种类型的系统设备所体现。例如，装置相机的个人计算机，装置相机的便携式终端机，基于Windows mobile，Android，IOS，Symbian，BlackBerry、BADA等系统的平板PC，平板电脑，PDA，智能手机，进而可在内部应用程序运行的OS数码相机就是典型的例子。
图3a是把图2图示的设备用硬件表现的示意，参考图3，生物信号检测装置(30)是摄像素子影像拍摄部分，也就是镜头(31)，从照相机(31)，照相机(32)的模拟影像信号数字化的A/D转换器(32)、执行图像分析及参数筛选、生物信号发生等处理器(34)，还具备着显示其结果的显示部分(35)。还包括为外来信息输入的输入设备，如同数字键盘等键输入部分(36)，以及在上述视频信号处理等使用的含内存的存储部分(33)。
图3b是可实现图2装置的大致构成图示，说明构成要素之间相互作用关系的图解。图3b中，参考号1是无线信号接收器和发报机及音频/视频信号，信号－音频/视频转换等在内的电子模块(Electronic Block)。参考号42中，从被测试对象(被测试者或试验者)获取视频摄像素子和接物镜等包括在内的数码相机。参考号43是智能手机全运用所需要的，尤其是为数字图像存储的内存的存储器。
参考号44为用户界面及处理结果等文字和图片形式视觉化的显示装置。参考号45上述电子砌块、数码相机、存储器、显示器等各功能的控制和同步处理器。另一方面，参考号46是整个系统驱动所需的电源和用户的输入等包括数字键盘在内的本体。
图4是根据本发明的生物信号视频为基础的通过相机获取过程流程图，这一过程可通过图3a，具体图3b装置利用。
图4为参考，首先，从摄相机(31)中获取被测试者的影像，转化为模拟前期信号(S10，S20)。从被测试者的影像获取的信号是前期模拟信号，因此通过A/D转换器(逆变器)(32)转化成数字视频数据(S30)。下一步，处理器(34)对各图像数据按时间的变化分析计算振动参数(S40)。振动参数根据上述被测试者的不同部位的位置变化对振动频率、振幅及相位包括其中的至少一个。即处理器(34)分析被测试试者的各部位的位置变化，分析各部位的振动频率、位置变化的大小(振动的大小)及相位等计算。
因此，处理器(34)利用振动图像分析程序分析图像间的差异，对位置重心的变化测量或利用傅立叶变换计算振动参数。对振动参数的计算详细说明如下。处理器(34)从连续多个视频当根据被测试者的轮廓变化或振动动态进行确认后，对轮廓均匀的分离为(左，右侧)。其次，在分成一半的横向两个部分中决定产生最大振动频率的地点。此频率决定生物信号图像的相应水平横向颜色。位于分离轮廓部位的分成两部分的横向位置变动平均振幅的生物信号图像大小(长度)。从各各地点获取的振动图像具有一定确实性和静态特征，但集成的生物信号图像是与人体精神生物学参数相关。处理器(34)根据算出的振动参数为基础，生成生物信号图像。(S50)。生物信号图像可包括振幅组成要素和频率组成要素。以下，振幅组成要素称为“内部生物信号图像”，频率组成要素称为“生物信号图像”。此类术语的概念和定义可 通过图5中的说明进行了解。最终，处理器(34)通过算出的振动参数获取被测试者的精神生理学信息(S60)。即，处理器(34)可通过分析振动参数了分析对象(20)的心理状态。
图5a是以振动图像的振幅成分而形成的人体图像周围产生的生物能量、气场辐射图示。内部生物信号图像是根据上述各部位的位置变化大小、由颜色来表现。通过这些对被测试者(1)位置变化的大小进行视觉化。外部生物图像出现在内部生物信号图像的周围，用颜色转变表现平均最高振动频率。图5b是图示人体的实际影像周围放射生物能量生物信号图像。图5b中，没有表现内部生物信号图像只显示实际影像周围生物信号图像。
图6a，6b是各表示稳定状态和非稳定状态下的生物信号图像，图6a是稳定或正常状态，而且图6b是压力状态下的被测试对象的生物信号图像。图6a中，生物信号图像从形态和颜色方面完全形成对称，生物信号图像的颜色显示为选择的颜色刻度(整体颜色－绿色)的中度。通过这些生物信号图像可以知道被测试对象在稳定的状态。相反图6b中，生物信号图像上的气场包含着很多红色成分。因此，在这种状态下可以知道被测试对象在不稳定状态。任何人受到刺激，例如通过屏幕暴露暴力镜头，被测试对象在受压力或攻击性状态时，生物信号图像的颜色将变成更加深的红颜色。图7a是在稳定状态的人体振动图像的频率构成因素(生物信号图像)的分布图，图7b是对压力状态下的人体振动图像的频率构成因素(生物信号图像)的分布图。图6a显示的曲线图是正常工作状态下人的典型频率分布图。研究表明，大多数人在平稳的状态下，普遍会出现与单模式分布规律相似的分布值。从屏幕中看到暴力镜头相似的受到特定负面影响时被测试者的情况会变为图6b所示。如果恐惧、压力及攻击性状态下频率分布(M)的平均值(中间)往增加的方向移动。稳定平和的状态下频率分布数值的(M)平均(中间)数值往减少的方向移动。频率轴(X)不仅可以用相对单位表达、也可以用实际单位或时间(Hz或sec。)来表达。显示值之间的距离是由对相机的迅速处理的实际参数和软件的设定(处理程序，图像存储和图像的时间)来决定。
目前为止，说明了各种模范实施例子另附加了示意。然而，这些实施例子只是各种实施例子的一部分。多种不同变化的技术领域下拥有传统知识的人也会产生。
上述说明示出并描述了本申请的若干优选实施例，但如前所述，应当理解本申请并非局限于本文所披露的形式，不应看作是对其他实施例的排除，而可用于各种其他组合、修改和环境，并能够在本文所述申请构想范围内，通过上述教导或相关领域的技术或知识进行改动。而本领域人员所进行的改动和变化不脱离本申请的精神和范围，则都应在本申请所附权利要求的保护范围内。

Claims (10)

  1. 一种基于视频精神生理参数监测危险人物装置，包括：
    获取单元，用于获取被测试人视频信息；
    生成单元，用于对所述视频信息进行处理，以生成包含危险程度相关的生物信号；
    处理单元，用于根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
  2. 根据权利要求1所述的基于视频精神生理参数监测危险人物装置，其特征在于，所述获取单元包括摄影模块。
  3. 根据权利要求2所述的基于视频精神生理参数监测危险人物装置，其特征在于，所述生成单元包括：图像数据A/D转换模块，所述图像数据A/D转换模块连接过滤模块，所述过滤模块连接生物图像显示模块。
  4. 根据权利要求3所述的基于视频精神生理参数监测危险人物装置，其特征在于，所述处理单元包括：CPU，其连接显示模块，存储模块与接口模块。
  5. 一种基于视频精神生理参数监测危险人物方法，其特征在于，包括：
    获取被测试人视频信息；
    对所述视频信息进行处理，以生成包含危险程度相关的生物信号；
    根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
  6. 根据权利要求5所述的基于视频精神生理参数监测危险人物方法，其特征在于，所述得到被测试人的危险程度基于以下算法：
    Figure PCTCN2016102065-appb-100001
    Figure PCTCN2016102065-appb-100002
    －目标左侧部分的“I”热振动图像总振幅
    Figure PCTCN2016102065-appb-100003
    －目标右侧部分的“I”热振动图像总振幅
    Figure PCTCN2016102065-appb-100004
    开始到
    Figure PCTCN2016102065-appb-100005
    之间的最大值
    Figure PCTCN2016102065-appb-100006
    －目标左侧部分的“I”振动图像最大频率
    Figure PCTCN2016102065-appb-100007
    －目标右侧部分的“I”热振动图像的最大频率
    Figure PCTCN2016102065-appb-100008
    开始到
    Figure PCTCN2016102065-appb-100009
    之间的最大值
    n–目标占最大的热值
  7. 根据权利要求5所述的基于视频精神生理参数监测危险人物方法，其特征在于，所述得到被测试人的危险程度基于以下算法：
    Figure PCTCN2016102065-appb-100010
    FM－频率分布密度直方图的最大频率
    Fi－没时间段50帧来获取频率分布密度的直方图i的频率数的统计计算数
    Fin－振动图像处理频率
    n－50帧还有高的限制值得帧之间的差异统计计算数
  8. 根据权利要求5所述的基于视频精神生理参数监测危险人物方法，其特征在于，所述得到被测试人的危险程度基于以下算法：
    Figure PCTCN2016102065-appb-100011
    Dn－危险状态水平，Ag－攻击性水平，St－压力水平，Tn－焦虑感水平，N－参数的一般平均值
  9. 根据权利要求5所述的基于视频精神生理参数监测危险人物方法，其特征在于，所述得到被测试人的危险程度基于以下算法：
    Figure PCTCN2016102065-appb-100012
    Tn–焦虑感水平
    Pi(f)–振动图像频率扩散动力光谱
    fmax–振动图像频率扩散光谱最大频率
  10. 一种基于视频精神生理参数监测危险人物方法，其特征在于，包括：
    获取被测试人连续产生变化的视频流信息；
    对所述视频流信息进行处理，以生成包含危险程度相关的生物信号；
    根据所述生物信号的振动频率，和/或振幅，得到被测试人的危险程度。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110693508A (zh) * 2019-09-02 2020-01-17 中国航天员科研训练中心 多通道协同的心理生理主动感知方法及服务机器人

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102198294B1 (ko) * 2018-08-28 2021-01-05 (주)마인드아이 비접촉 영상 시스템을 이용한 뇌피로 평가 방법 및 장치
CN109528217A (zh) * 2018-10-16 2019-03-29 北京入思技术有限公司 一种基于生理振动分析的情绪检测和预警方法
CN111104815A (zh) * 2018-10-25 2020-05-05 北京入思技术有限公司 一种基于情绪能量感知的心理测评方法及装置
CN109643376A (zh) * 2018-11-02 2019-04-16 金湘范 视频采集情感生成方法
CN109902574A (zh) * 2019-01-24 2019-06-18 北京元和新通科技有限公司 人体表象变化测定人体情绪的高危人员检测装置及方法
CN109551500A (zh) * 2019-01-29 2019-04-02 南京奥拓电子科技有限公司 机器人监控报警系统
CN111631735A (zh) * 2020-04-26 2020-09-08 华东师范大学 基于视频数据振动频率的异常情绪监测预警方法
CN112515673A (zh) * 2020-11-30 2021-03-19 重庆工程职业技术学院 心理危机干预辅助系统
CN112775983A (zh) * 2020-12-25 2021-05-11 张月崧 智能防卫保护方法及智能保镖机器人
CN112722110A (zh) * 2020-12-25 2021-04-30 张月崧 智慧保镖机器狗
CN114973485A (zh) * 2021-02-25 2022-08-30 深圳埃尔姆科技有限公司 可疑人员门禁识别方法、装置及系统
CN117373089A (zh) * 2023-10-23 2024-01-09 北京阳光易德心理学应用技术有限公司 一种基于计算机视觉的面部情绪识别装置

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104644187A (zh) * 2015-01-16 2015-05-27 广微科技集团有限公司 一种基于红外热像仪的测谎方法和系统
US20160015308A1 (en) * 2012-02-28 2016-01-21 Koninklijke Philips N.V. Device and method for monitoring vital signs
CN105913046A (zh) * 2016-05-06 2016-08-31 姜振宇 微表情识别装置及方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8850597B1 (en) * 2013-03-14 2014-09-30 Ca, Inc. Automated message transmission prevention based on environment
KR101536348B1 (ko) * 2014-03-31 2015-07-14 주식회사 바이브라시스템 동영상 기반 생리 신호를 이용한 졸음 검출 방법 및 장치
KR101739058B1 (ko) * 2014-04-24 2017-05-25 주식회사 바이브라시스템 동영상 기반 생리 신호 검출을 이용한 왜곡에 대한 정신생리적 탐지 (거짓말 탐지) 방법 및 장치
CN105427518B (zh) * 2015-11-10 2017-08-01 成都智慧数联信息技术有限公司 一种数据化危险判决系统及方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160015308A1 (en) * 2012-02-28 2016-01-21 Koninklijke Philips N.V. Device and method for monitoring vital signs
CN104644187A (zh) * 2015-01-16 2015-05-27 广微科技集团有限公司 一种基于红外热像仪的测谎方法和系统
CN105913046A (zh) * 2016-05-06 2016-08-31 姜振宇 微表情识别装置及方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110693508A (zh) * 2019-09-02 2020-01-17 中国航天员科研训练中心 多通道协同的心理生理主动感知方法及服务机器人

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