WO2024057356A1 - 開瞼度検出装置、開瞼度検出方法、および眠気判定システム - Google Patents

開瞼度検出装置、開瞼度検出方法、および眠気判定システム Download PDF

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Publication number
WO2024057356A1
WO2024057356A1 PCT/JP2022/033982 JP2022033982W WO2024057356A1 WO 2024057356 A1 WO2024057356 A1 WO 2024057356A1 JP 2022033982 W JP2022033982 W JP 2022033982W WO 2024057356 A1 WO2024057356 A1 WO 2024057356A1
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WIPO (PCT)
Prior art keywords
eyelid opening
histogram
degree
occupant
eyelid
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Ceased
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PCT/JP2022/033982
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English (en)
French (fr)
Japanese (ja)
Inventor
和樹 國廣
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Mitsubishi Electric Corp
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Mitsubishi Electric Corp
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Publication date
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Priority to PCT/JP2022/033982 priority Critical patent/WO2024057356A1/ja
Priority to JP2024546520A priority patent/JP7812001B2/ja
Publication of WO2024057356A1 publication Critical patent/WO2024057356A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems

Definitions

  • the present disclosure relates to eyelid opening degree detection technology.
  • Patent Document 1 discloses a doze determination device and an eyelid detection device that constitutes the doze determination device, and one embodiment of the eyelid detection device includes an eye opening degree calculation unit.
  • an eye opening calculation unit calculates the vertical distance of the eye area in the normal state using information on the driver's eyes in the normal state, and calculates this distance when the eye opening is 100%. It is stated that this is the reference distance.
  • the eye opening degree is also referred to as the eyelid opening degree, and in this disclosure, the term eyelid opening degree is used.
  • the present disclosure has been made to solve such problems, and aims to provide an eyelid opening degree detection technique that can appropriately calculate the eyelid opening degree to be used as a standard.
  • An eyelid opening degree detection device includes an in-vehicle image acquisition unit that acquires an image of an occupant inside a vehicle, and a plurality of frame images included in the image based on the image of the occupant.
  • an eyelid opening degree calculation unit that calculates an eyelid opening degree indicating the eyelid opening degree of the occupant; and a histogram creation unit that creates a histogram of the eyelid opening degree in the eyelid open state of the occupant based on the calculated eyelid opening degree.
  • a histogram evaluation unit that evaluates whether the created histogram includes false detection data and outputs the evaluation result as a histogram evaluation result;
  • a personal feature amount calculation unit that calculates the personal feature amount of the personal feature amount.
  • the eyelid opening degree detection technology it is possible to appropriately calculate the eyelid opening degree to be used as a standard.
  • FIG. 2 is a block diagram showing a configuration example of an eyelid opening degree detection device and a drowsiness determination system.
  • FIG. 2 is a diagram showing an example of a hardware configuration of an eyelid opening degree detection device and a drowsiness determination system.
  • FIG. 2 is a diagram showing an example of a hardware configuration of an eyelid opening degree detection device and a drowsiness determination system. It is a flowchart showing the operation of the eyelid opening degree detection device.
  • FIG. 2 is a schematic diagram of the eye for explaining the degree of eyelid opening. This is an example of a histogram of flattening ratio as the degree of eyelid opening.
  • FIG. 1 is a block diagram showing a configuration example of an eyelid opening degree detection device 31 and a drowsiness determination system 30 according to Embodiment 1 of the present disclosure.
  • the vehicle V includes an imaging device 10, a vehicle information acquisition device 20, and a drowsiness determination system 30.
  • the drowsiness determination system 30 also includes an eyelid opening degree detection device 31 and a drowsiness determination device 32.
  • the drowsiness determination device 32 determines whether or not the occupant is currently feeling drowsy, using the standard eyelid opening degree of the occupant calculated by the eyelid opening degree detection device 31 . For example, the drowsiness determining device 32 determines that the occupant is drowsy when the ratio of the occupant's current eyelid opening degree to the occupant's standard eyelid opening degree is equal to or less than a predetermined threshold. Note that the current degree of eyelid opening of the occupant is calculated by, for example, an eyelid opening degree calculating section 312 described below, and the drowsiness determining device 32 acquires the current degree of eyelid opening calculated by the eyelid opening degree calculating section 312.
  • the imaging device 10 is a device for imaging the occupant of the vehicle V.
  • the imaging device 10 is installed, for example, in the front part of the vehicle interior of the vehicle V, and images an area including the face of an occupant such as a driver of the vehicle V from the front.
  • the imaging device 10 includes one visible light camera, multiple visible light cameras, one infrared camera, or multiple infrared cameras.
  • a light source (not shown) is provided that irradiates an area including the driver's face with infrared rays for imaging.
  • This light source is composed of, for example, an LED (Light Emitting Diode).
  • the imaging device 10 outputs a video composed of a plurality of captured frame images to an eyelid opening degree detection device 31 included in the drowsiness determination system 30.
  • the vehicle information acquisition device 20 is a sensor that acquires the driving state of the vehicle V. Examples of the driving state include vehicle speed, steering angle, or shift information.
  • the vehicle information acquisition device 20 outputs the acquired information regarding the driving state to the eyelid opening degree detection device 31 included in the drowsiness determination system 30.
  • the eyelid opening degree detection device 31 is a device for calculating the standard eyelid opening degree of the occupant. Since there are individual differences in the degree of eye opening, it is necessary to calculate the degree of eyelid opening of the occupant to be monitored by the drowsiness determination system 30 when the eyes are open. Therefore, the eyelid opening degree detection device 31 calculates the eyelid opening degree in the eye open state used by the drowsiness determination system 30, that is, the standard eyelid opening degree. In order to realize such a function, the eyelid opening degree detection device 31 includes an in-vehicle image acquisition section 311, an eyelid opening degree calculation section 312, a histogram creation section 313, a histogram evaluation section 314, and a personal feature amount calculation section 315. . Further, the eyelid opening degree detection device 31 includes a control section (not shown) as a functional section that controls the overall operation of the eyelid opening degree detection device 31.
  • the in-vehicle video acquisition unit 311 acquires the video output by the imaging device 10.
  • the eyelid opening degree calculation unit 312 calculates the degree of eyelid opening for each frame image included in the video acquired by the in-vehicle video acquisition unit 311.
  • the degree of eyelid opening is an index indicating the degree to which the eyes are opened.
  • the degree of eyelid opening is determined by the distance Ev between the straight line connecting the inner and outer corner coordinates and the highest point of the upper eyelid, and the distance Eh between the inner and outer corner coordinates. This is the flattening ratio calculated by dividing by .
  • the highest point of the upper eyelid is the point (apex) of the upper eyelid that is farthest from the straight line connecting the inner corner coordinates and the outer corner coordinates.
  • the eyelid opening degree calculation unit 312 obtains the inner corner coordinates, outer corner coordinates, and upper eyelid apex coordinates using, for example, the results of machine learning. In other words, the eyelid opening degree calculation unit 312 inputs each frame image into a trained model that has machine-learned the relationship between an image including a face and the feature points of the inner corner of the eye, the outer corner of the eye, and the upper eyelid, thereby calculating the inner corner coordinates, etc. Get location information.
  • the eyelid opening degree calculation unit 312 may detect a face area using a known algorithm such as Haar-Like, and calculate feature points from the detected face area on a program basis using a known image processing technique. .
  • the eyelid opening degree calculation unit 312 may acquire positional information such as the inner corner coordinates of the eyes by combining a machine learning model and a program-based model.
  • a machine learning model may be used to obtain positional information of inner corner coordinates and outer corner coordinates
  • image processing may be used to obtain the upper eyelid apex.
  • the upper eyelid apex is determined by, for example, detecting the edge of the upper eyelid through image processing using a differential filter, performing curve fitting on the detected points to detect the upper eyelid line, and calculating the coordinates of the upper eyelid apex.
  • the distance between the eyelids which is the distance between the upper eyelid and the lower eyelid, may be used as the degree of eyelid opening.
  • the distance between the upper eyelid apex and the lower eyelid apex may be used.
  • the lower eyelid apex may be calculated using a method similar to the method used to calculate the upper eyelid apex.
  • the eyelid opening degree calculation unit 312 does not need to calculate the degree of eyelid opening for frames related to samples that are not suitable as samples for the degree of eyelid opening. For example, when a passenger wears glasses, a reflected image of the scenery is reflected on the lens of the glasses, and this reflected image of the scenery can become an impediment to calculating the degree of eyelid opening. Therefore, the eyelid opening degree calculation unit 312 does not need to calculate the degree of eyelid opening for frames with obstructive factors such as scenery reflections.
  • the eyelid opening degree calculation unit 312 is configured to acquire both eyes of the passenger from the in-vehicle image acquisition unit 311, for example, and when only one eye of the passenger can be acquired, it is possible to detect landscape reflections. It may be determined that there is. Generally, an occupant is seated on either side of the left or right door of a vehicle, so scenery reflections often occur only on one of the lenses of the glasses. Therefore, if only one eye of the occupant can be acquired, it can be assumed that a landscape reflection has occurred on the glass of the glasses in front of the other eye. The eyelid opening degree calculation unit 312 does not need to calculate the degree of eyelid opening even for samples for which the reliability of the degree of eyelid opening is considered to be low due to inhibiting factors other than the scenery reflection described above.
  • the histogram creation unit 313 When determining that the vehicle is running, the histogram creation unit 313 creates a histogram of the degree of eyelid opening of the occupant in the eye-open state based on the degree of eyelid opening calculated by the degree of eyelid opening calculation unit 312. Based on the created histogram of the degree of eyelid opening, the degree of eyelid opening used as a standard is calculated in subsequent processing.
  • the histogram creation unit 313 creates a histogram of the eyelid opening degree without using the eyelid opening degree of that frame. For example, if the value of the degree of eyelid opening is less than or equal to a predetermined threshold value of 0.05, the histogram of the degree of eyelid opening is created without using the value of the degree of eyelid opening.
  • the histogram creation unit 313 determines whether the vehicle is running based on information regarding the running state acquired by the vehicle information acquisition device 20. For example, if the vehicle speed is equal to or higher than a predetermined threshold, such as 10 km/h, 15 km/h, or 20 km/h, it may be determined that the vehicle is running. Regarding the steering angle, for example, if the steering angle is within a predetermined range such as ⁇ 10 degrees from the front of the vehicle, it may be determined that the vehicle is running. Further, regarding the shift information, if it is in drive mode, it may be determined that the vehicle is running. It may be determined that the vehicle is running when two or more of the conditions related to vehicle speed, steering angle, and shift information are satisfied.
  • a predetermined threshold such as 10 km/h, 15 km/h, or 20 km/h
  • the steering angle for example, if the steering angle is within a predetermined range such as ⁇ 10 degrees from the front of the vehicle, it may be determined that the vehicle is running.
  • the shift information if it
  • FIG. 5 shows an example of a histogram created by the histogram creation unit 313.
  • FIG. 5 shows an example of a histogram when flatness is used as the degree of eyelid opening. That is, a histogram is shown in which the horizontal axis is the flatness ratio and the vertical axis is the frequency. In the histogram of FIG. 5, peaks are formed at two positions where the oblateness value is 0.21 and 0.42. Note that when the distance between the eyelids is used as the degree of eyelid opening, a histogram is created using the distance between the eyelids as a variable.
  • the samples used to create the histogram include erroneously detected samples in which the upper eyelid was not detected correctly.
  • Examples of false positive factors include eyelashes, eyelash extensions, eye makeup, fullness in the upper eyelid area, and deep eye carvings. At least one of these factors will result in false positives.
  • the histogram evaluation unit 314 evaluates whether the histogram created by the histogram creation unit 313 includes false detection data. This evaluation is performed based on whether the number of peaks included in the histogram is one. If the number of peaks included in the histogram is one, the histogram evaluation unit 314 evaluates that the histogram does not include false detection data. On the other hand, if the number of peaks included in the histogram is not one, that is, if the number of peaks included in the histogram is two, the histogram evaluation unit 314 evaluates that the histogram includes false detection data. . The histogram evaluation unit 314 outputs an evaluation result indicating whether the histogram includes false detection data as a histogram evaluation result.
  • the personal feature calculation unit 315 calculates the individual feature of the occupant based on the histogram created by the histogram creation unit 313 and in accordance with the histogram evaluation result of the histogram evaluation unit 314.
  • the individual feature amount of the passenger means the standard degree of opening of the passenger's eyelids.
  • the personal feature calculation unit 315 calculates the value of the most frequent eyelid opening degree with the highest frequency as the passenger's personal feature.
  • the personal feature calculation unit 315 calculates the relationship between the peak of the most frequent degree of eyelid opening and the peak of the least frequent degree of eyelid opening. Accordingly, the individual characteristic amount of the occupant is calculated. Specifically, when there is a peak in which the value of the degree of eyelid opening is larger than the value of the most frequent degree of eyelid opening, the personal feature amount calculation unit 315 calculates the value of the most frequent degree of eyelid opening as the personal feature amount of the occupant. .
  • the personal feature amount calculation unit 315 calculates the value of the degree of eyelid opening of the peak where the value of the degree of eyelid opening is smaller than the value of the degree of eyelid opening of the passenger. Calculate as individual feature quantity.
  • the personal feature calculation unit 315 selects the smaller value of the degree of eyelid opening forming the two peaks as the personal feature of the passenger. calculate. By calculating the individual characteristic amount of the occupant in this manner, it is possible to appropriately calculate the degree of eyelid opening used as a standard.
  • the drowsiness determination device 32 determines whether the occupant is currently feeling sleepy.
  • Each functional unit of the eyelid opening degree detection device 31 is realized by a processing circuit.
  • a processing circuit even if it is a dedicated processing circuit 100a as shown in FIG. 2A, executes a program stored in a memory 100c as shown in FIG. 2B. It may be the processor 100b.
  • the dedicated processing circuit 100a is, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, or an application specific integrated circuit (ASIC). , FPGA (field-programmable gate array), or a combination of these.
  • Each functional unit may be realized by a plurality of separate processing circuits, or each functional unit may be realized by a single processing circuit.
  • each functional unit is realized by software, firmware, or a combination of software and firmware.
  • Software and firmware are written as programs and stored in memory 100c.
  • the processor 100b implements each functional unit by reading and executing programs stored in memory. Examples of the memory 100c include non-volatile or Includes volatile semiconductor memory, magnetic disks, flexible disks, optical disks, compact disks, minidisks, and DVDs.
  • the processing circuit can implement each functional unit using hardware, software, firmware, or a combination thereof.
  • the hardware of the drowsiness determination device 32 can also be configured similarly to the hardware of the eyelid opening degree detection device 31.
  • step ST0 the in-vehicle image acquisition unit 311 acquires, from the imaging device 10, an image of the occupant inside the vehicle V, which is imaged by the imaging device 10.
  • step ST1 the control unit (not shown) of the eyelid opening degree detection device 31 determines whether the calculation of the personal feature amount has been completed. That is, it is determined whether the personal feature amount calculation unit 315 has calculated the personal feature amount. If the calculation of the personal feature amount has been completed, the process ends. If the calculation of the personal feature amount is not completed, the process proceeds to step ST2.
  • step ST2 the eyelid opening degree calculation section 312 calculates the degree of eyelid opening for a plurality of frames included in the acquired video, and the histogram creation section 313 calculates the degree of eyelid opening from the plurality of degrees of eyelid opening calculated by the eyelid opening degree calculation section 312. Create a histogram of eyelid opening degree.
  • step ST3 the histogram creation unit 313 determines whether the accumulation of the degree of eyelid opening is completed. That is, the histogram creation unit 313 determines whether to finish creating the histogram of the degree of eyelid opening.
  • the condition for completing the creation of the histogram is that a predetermined number of samples, for example, several hundred samples, have been obtained. If the accumulation of the degree of eyelid opening is not completed, the process returns to step ST0 and an image for calculating the degree of eyelid opening is acquired. When the accumulation of the degree of eyelid opening is completed, the process proceeds to step ST4.
  • step ST4 the histogram evaluation unit 314 searches for a peak in the histogram of the degree of eyelid opening created by the histogram creation unit 313. That is, the histogram evaluation unit 314 searches for peaks and counts the number of peaks.
  • step ST5 the histogram evaluation unit 314 determines whether the number of peaks obtained through the search is one. Through this determination, it is evaluated whether the created histogram of the degree of eyelid opening includes false positive data. If there is not one peak, that is, if there are two peaks, the histogram evaluation unit 314 evaluates that the histogram includes false positive data, and the process proceeds to step ST6. On the other hand, if there is one peak, the histogram evaluation unit 314 evaluates that the histogram does not include false detection data, and the process proceeds to step ST7.
  • step ST6 the personal feature value calculation unit 315 determines whether there is a peak on the side where the flatness rate (the degree of eyelid opening) is smaller than the most frequent flatness rate (the most frequent degree of eyelid opening). That is, the histogram evaluation unit 314 determines whether another peak that is not the most frequent is formed at a position where the value of the oblateness is smaller than the value of the most frequent oblateness. If the result of the determination is No, the process proceeds to step ST7. If the result of the determination is Yes, the process proceeds to step ST8.
  • step ST7 the personal feature calculation unit 315 calculates the value of the mode flatness as the personal feature.
  • step ST8 the personal feature amount calculation unit 315 calculates the value of the flatness ratio of the other peak as the personal feature amount. That is, the personal feature amount calculation unit 315 calculates the value of the flatness of the other peaks that are not the highest in frequency as the individual feature amount of the occupant.
  • the eyelid opening degree detection device (100) of Supplementary Note 1 includes an in-vehicle image acquisition unit (311) that acquires an image of an occupant inside the vehicle, and an in-vehicle image acquisition unit (311) that acquires an image of an occupant inside the vehicle, and a plurality of frame images included in the image based on the image of the occupant.
  • An eyelid opening degree calculation unit (312) that calculates an eyelid opening degree indicating the eyelid opening degree of the occupant, and a histogram of the eyelid opening degree in the eyelid open state of the occupant based on the calculated eyelid opening degree.
  • a histogram creation unit (313) that evaluates whether the created histogram includes false positive data, and outputs the evaluation result as a histogram evaluation result;
  • the vehicle includes a personal feature calculation unit (315) that calculates the personal feature of the occupant according to the histogram evaluation result.
  • the eyelid opening degree detection device of Appendix 2 is the eyelid opening degree detection device described in Appendix 1, wherein the eyelid opening degree is determined by the distance between the top of the upper eyelid and a straight line connecting the inner and outer corners of the occupant's eyes. is the flattening ratio divided by the distance of a straight line connecting the inner and outer corners of the eyes.
  • the eyelid opening degree detection device according to appendix 3 is the eyelid opening degree detection device described in appendix 1, wherein the eyelid opening degree is an interlid distance between the upper and lower eyelids of the occupant.
  • the eyelid opening degree detection device is the eyelid opening degree detection device described in any one of appendixes 1 to 3, wherein the histogram creation unit is configured to calculate the vehicle speed of the vehicle acquired by the vehicle information acquisition device. , the histogram is created when it is determined that the vehicle is running based on the steering wheel angle or shift information.
  • the eyelid opening degree detecting device is the eyelid opening degree detecting device described in any one of appendices 1 to 4, and the histogram evaluation unit, when there is only one peak in the histogram, When it is evaluated that the created histogram does not include false detection data, and the personal feature amount calculation unit evaluates that the created histogram does not contain false detection data, the calculated eyelid open Among the degrees, the most frequent degree of eyelid opening with the highest frequency is calculated as the individual characteristic amount of the occupant.
  • the eyelid opening degree detecting device is the eyelid opening degree detecting device described in any one of appendices 1 to 4, and the histogram evaluation unit, when two peaks exist in the histogram, When it is evaluated that the created histogram includes false detection data, and the personal feature amount calculation unit evaluates that the created histogram includes false detection data, the calculated degree of eyelid opening is: When there is a peak in which the degree of eyelid opening is larger than the most frequent degree of eyelid opening, the most frequent degree of eyelid opening is calculated as the personal feature amount of the passenger, and the degree of eyelid opening is smaller than the most frequent degree of eyelid opening. When there is a peak, the eyelid opening degree at the peak where the eyelid opening degree is small is calculated as the individual feature amount of the occupant.
  • the drowsiness determination system determines the drowsiness of the occupant based on the eyelid opening degree detection device (31) described in any one of appendices 1 to 6 and the calculated personal characteristic amount of the occupant.
  • a drowsiness determination device (32) is provided.
  • the eyelid opening degree detection method in Appendix 8 includes an in-vehicle image acquisition unit (311), an eyelid opening degree calculation unit (312), a histogram creation unit (313), a histogram evaluation unit (314), and a personal feature value calculation unit (315).
  • An eyelid opening degree detection method performed by an eyelid opening degree detection device (31) comprising: a step (ST0) in which the in-vehicle image acquisition section acquires an image of an occupant inside the vehicle; is a step (ST2) of calculating an eyelid opening degree indicating the degree of opening of the eyes of the occupant for a plurality of frame images included in the image based on the image of the occupant; a step (ST2) of creating a histogram of the degree of eyelid opening of the occupant in the eye-open state based on the degree of eyelid opening of the passenger; and the histogram evaluation unit determining whether or not the created histogram includes false detection data.
  • a step of evaluating and outputting the evaluation result as a histogram evaluation result (ST4 to ST6), and the personal feature amount calculation unit calculates the individual feature amount of the occupant according to the outputted histogram evaluation result. Steps (ST7 to ST8) are provided.
  • the eyelid opening degree detection technology of the present disclosure can be used as a technology for obtaining a standard eyelid opening degree of a passenger such as a driver used in a PMS (Passenger Monitoring System).
  • PMS Passenger Monitoring System
  • 10 imaging device 20 vehicle information acquisition device, 30 drowsiness determination system, 31 eyelid opening degree detection device, 32 drowsiness determination device, 100a processing circuit, 100b processor, 100c memory, 311 in-vehicle image acquisition unit, 312 eyelid opening degree calculation unit, 313 Histogram creation unit, 314 Histogram evaluation unit, 315 Personal feature amount calculation unit.

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PCT/JP2022/033982 2022-09-12 2022-09-12 開瞼度検出装置、開瞼度検出方法、および眠気判定システム Ceased WO2024057356A1 (ja)

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JP2024546520A JP7812001B2 (ja) 2022-09-12 2022-09-12 開瞼度検出装置、開瞼度検出方法、および眠気判定システム

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018134875A1 (ja) * 2017-01-17 2018-07-26 三菱電機株式会社 瞼検出装置、居眠り判定装置、および瞼検出方法
WO2018150485A1 (ja) * 2017-02-15 2018-08-23 三菱電機株式会社 運転状態判定装置および運転状態判定方法
WO2019198179A1 (ja) * 2018-04-11 2019-10-17 三菱電機株式会社 搭乗者状態判定装置、警告出力制御装置及び搭乗者状態判定方法
WO2020174601A1 (ja) * 2019-02-27 2020-09-03 三菱電機株式会社 覚醒度推定装置、自動運転支援装置および覚醒度推定方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018134875A1 (ja) * 2017-01-17 2018-07-26 三菱電機株式会社 瞼検出装置、居眠り判定装置、および瞼検出方法
WO2018150485A1 (ja) * 2017-02-15 2018-08-23 三菱電機株式会社 運転状態判定装置および運転状態判定方法
WO2019198179A1 (ja) * 2018-04-11 2019-10-17 三菱電機株式会社 搭乗者状態判定装置、警告出力制御装置及び搭乗者状態判定方法
WO2020174601A1 (ja) * 2019-02-27 2020-09-03 三菱電機株式会社 覚醒度推定装置、自動運転支援装置および覚醒度推定方法

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