CN105334960A - Vehicle-mounted intelligent gesture recognition system - Google Patents
Vehicle-mounted intelligent gesture recognition system Download PDFInfo
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- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
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Abstract
The invention discloses a vehicle-mounted intelligent gesture recognition system. According to different application backgrounds, gesture recognition modules can be different; the gesture detection and tracking is the forefront processing part in the gesture recognition processing flow process, processes a gesture image or sequence obtained from a camera, and detects and divides a gesture object; different gesture models have different gesture features; the static gesture recognition and dynamic gesture recognition features are also different. The vehicle-mounted intelligent gesture recognition system has the advantages that the gesture command of personnel in a vehicle can be automatically recognized; the operation on the gesture command is convenient; the time of leaving the two hands away from a steering wheel by a driver is reduced; safety and reliability are higher; corresponding instructions can be obtained according to the finger action of the driver; the operation is convenient; the driving safety is not influenced.
Description
Technical field
The present invention relates to automotive field, particularly, relate to a kind of vehicle intelligent gesture recognition system.
Background technology
Automobile is just like giving a definition: by power drive, has the vehicle of the non-track carrying of more than 4 or 4 wheels, is mainly used in: carrying personnel and or goods; The vehicle of traction carrying personnel or goods; Specific use.1879, German slip-stick artist Ka Erbenci, tested a tentative engine of two-stroke successfully first.In October, 1883, he has founded " Ben Ci company and Lai Yin gas motor factory ", 1885, he has made first this thatch patent motor vehicle in Mannheim, this car is tricar, adopt the gasoline engine of a two-stroke single cylinder 0.9 horsepower, this car has possessed some basic characteristics of Hyundai Motor, as spark ignition, Water-cooling circulating, steel pipe vehicle frame, Leaf Spring Suspension, rear wheel drive front-wheel steer and binding handle etc.On the January of 1886 29, the German slip-stick artist Ka Erbenci patent that has been its motor vehicle application.In November in the same year, the three-wheeled motor vehicle of Ka Erbenci obtains Germany's patent right.First Hyundai Motor in the world that Here it is generally acknowledges.For above-mentioned reasons, people generally all using 1886 as automobile first year, also some scholar makes Ka Erbenci that is 1885 year on first tricar, is considered as automobile and is born year.It within 1885, is 1 year that automobile invention obtains decisive breakthrough.At that time with the Ben Ci of Daimler at same factory, also at research automobile.He almost made petrol engine with Daimler in 1885 simultaneously, was contained on automobile, with the speeds of 12 kilometers per hour, succeeded.In this year, the Butler of Britain have also been invented gasoline-powered automobile.In addition, gondola Claude Bernard have also been invented automobile, Pu Qiluofu and Fu Luobofu two human hair of Russia understands the automobile that internal combustion engine is housed.China did not have auto manufacturing in the past.On Chinese soil, first automobile is that the U.S. inputted for 1903 produces oldsmobile board car, and lead to obtain number one running car licence, its owner is Shanghai rich man.From July nineteen fifty-three First Automobile Works start building, in July, 1956 goes into operation, July 13 nineteen fifty-seven, China produced the Jiefang brand automobile of first loading, again in May, 1958, China's First Automobile Works voluntarily Development and design produces first and to rise and fall the red flag board passenger car jolting, share weal or woe with political wind and cloud at that time, is described as " east romantic charm ".In decades, Domestic Automotive Industry obtains and develops fast.Particularly since reform and opening-up, automobile production have employed safety and the amenities of various high-tech and hommization, draws the elite of Foreign Automobile scientific research.Not only grasp and taken advantage of traditional firm moulding, have more the gentle and lovely style and features of fashion automobile, the lines flow smoothly, drives comfortable " car " new lover and be constantly born.At the bottom of calendar year 2001, China has become a full member of the World Trade Organization, and taking this as an opportunity, and Chinese Auto Industry has welcome a new high-speed developing period.2009, Chinese automobile production and marketing was respectively 1379.10 ten thousand and 1364.48 ten thousand, surmounted the U.S. at one stroke, was called the first in the world automobile production and marketing big country.The annual production and marketing of Chinese automobile in 2012 is respectively 1927.18 ten thousand and 1930.64 ten thousand, within continuous 4 years, continues to hold a post or title the first in the world.Enter after 10 years high speed developments, Chinese independent brand passenger car technology obtains significant progress.The upper vapour Roewe that one vapour red flag, the gentry of Beijing Automobile Workshop of the successively listing first half of the year in 2013 are precious, farsightedly gallop in Chang'an, lucky Deidro Deluxe, BYD think sharp and successively listing before this, Guangzhou Automobile Workshop pass the high-end passenger car of independent brand for representative such as auspicious and initiate group type to joint brand and charge, and will progressively rewrite independent brand passenger car and can only seize in low and middle-end the present situation in market.Hyundai Motor is of a great variety, and people are driving a car in driving process, needs to operate vehicle, and both hands can not departure direction dish, causes inconvenience.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of vehicle intelligent gesture recognition system, this system can identify the gesture command of occupant automatically, conveniently operate on it, decrease the time of human pilot both hands departure direction dish, safety is with reliable more, corresponding instruction can be obtained, handled easily according to driver's finger movement, not affect the security of driving.
The present invention's adopted technical scheme that solves the problem is: vehicle intelligent gesture recognition system, comprises the following steps:
(1) in gesture identification framework, gesture model is a meat and potatoes, according to different application backgrounds, the model adopted in gesture identification has difference, and for different gesture models, the gestures detection adopted and track algorithm, feature extraction, recognition technology also has difference, gesture modeling is mainly divided into based on apparent gesture model and the gesture model based on three-dimensional, a kind of two-dimentional modeling based on apparent gesture modeling, from the feature that the observable plane picture information of two dimensional surface is described in, the model based on color and two kinds, the model based on profile is mainly comprised based on apparent gesture model, gesture model based on color is set images of gestures being regarded as pixel color, by the feature of the color extracting hand, gesture is described, common feature based on the gesture model of color is color histogram, gesture model based on profile is that handle regards a profile as, by the geometric properties of the profile extracting hand in hand images, gesture is described,
(2) gestures detection is processing section foremost in gesture recognition process flow process with following the tracks of, its process gets images of gestures or sequence from camera, therefrom detect and dividing gesture object, if dynamic hand gesture recognition, opponent is also wanted to follow the tracks of, method based on movable information supposes to only have hand to be moving object in video, wherein a kind of method is background subtraction, it requires that stationary background is constant, the every frame in video and background subtracting, the part vanishing that background is identical, different parts just thinks the object moved, i.e. hand, another kind method is difference frame method, by present frame and former frame or front some frame subtract, detect that difference between two frames is to determine the initiation region of hand exercise, but the gesture motion amplitude between general consecutive frame is little, difference frame method can only detect the profile of motion, the hand that subtraction could correctly detect and segmentation is moved to be done with lower frame sampling rate or every some frames,
(3) extraction of gesture feature is closely-related with gesture model, different gesture models has difference to be had in gesture feature, static gesture identification is with dynamically also different in the feature of gesture identification, the feature of static gesture is the static information of the hand described, profile, area, dynamic gesture feature is continuous print static nature sequence; Static gesture knows method for distinguishing to be had a lot, and in method, the method for template matches, the method based on machine learning of rule, dynamic gesture has Time and place change, becomes temporal symbol sebolic addressing after gesture feature quantization encoding;
(4) based on the complexion model parameter initialization method of sampling, by to being collected in portion's image pattern, auto-initiation hand skin color model, hand area and face area parameter, the gesture tracking method under blocking based on centroid estimation hand face is described at gesture tracking, at gesture feature Extraction parts, use the finger characteristic extracting method based on profile, and extract motion vector as dynamic gesture feature, it is finally gesture identification part, static gesture identification have employed the gesture identification strategy cooperated based on orthogonal camera, dynamic hand gesture recognition then uses a kind of method of overlapping independently hidden Markov model in conjunction with two,
(5) input is the gesture video of two cameras, output is the hand gesture location and gesture command that identify, hand gesture location is wherein for being converted into system cursor position, gesture motion is mapped as the keyboard shortcut of application software, by gesture input being converted into the input of the standard device of system, system uses two common cameras, the better noise of video quality of camera is fewer, process will become more accurate, system employs two cameras, one of them is arranged on above the screen of front, from forward observation gesture motion, another is arranged on bottom, observe gesture motion from below, bottom camera can according to specific environment condition, selection is arranged on top, subscriber station stands in screen front, just camera is put to front-facing camera and the end, with front-facing camera apart from about 0.2 meter, the below of the position when hand that bottom camera position is adjusted to user stretches out.
To sum up, the invention has the beneficial effects as follows: this system can identify the gesture command of occupant automatically, conveniently operate on it, decrease the time of human pilot both hands departure direction dish, safety is with reliable more, corresponding instruction can be obtained, handled easily according to driver's finger movement, not affect the security of driving.
Embodiment
Below in conjunction with embodiment, to the detailed description further of the present invention's do, but embodiments of the present invention are not limited thereto.
Embodiment:
Vehicle intelligent gesture recognition system, comprises the following steps:
(1) in gesture identification framework, gesture model is a meat and potatoes, according to different application backgrounds, the model adopted in gesture identification has difference, and for different gesture models, the gestures detection adopted and track algorithm, feature extraction, recognition technology also has difference, gesture modeling is mainly divided into based on apparent gesture model and the gesture model based on three-dimensional, a kind of two-dimentional modeling based on apparent gesture modeling, from the feature that the observable plane picture information of two dimensional surface is described in, the model based on color and two kinds, the model based on profile is mainly comprised based on apparent gesture model, gesture model based on color is set images of gestures being regarded as pixel color, by the feature of the color extracting hand, gesture is described, common feature based on the gesture model of color is color histogram, gesture model based on profile is that handle regards a profile as, by the geometric properties of the profile extracting hand in hand images, gesture is described,
(2) gestures detection is processing section foremost in gesture recognition process flow process with following the tracks of, its process gets images of gestures or sequence from camera, therefrom detect and dividing gesture object, if dynamic hand gesture recognition, opponent is also wanted to follow the tracks of, method based on movable information supposes to only have hand to be moving object in video, wherein a kind of method is background subtraction, it requires that stationary background is constant, the every frame in video and background subtracting, the part vanishing that background is identical, different parts just thinks the object moved, i.e. hand, another kind method is difference frame method, by present frame and former frame or front some frame subtract, detect that difference between two frames is to determine the initiation region of hand exercise, but the gesture motion amplitude between general consecutive frame is little, difference frame method can only detect the profile of motion, the hand that subtraction could correctly detect and segmentation is moved to be done with lower frame sampling rate or every some frames,
(3) extraction of gesture feature is closely-related with gesture model, different gesture models has difference to be had in gesture feature, static gesture identification is with dynamically also different in the feature of gesture identification, the feature of static gesture is the static information of the hand described, profile, area, dynamic gesture feature is continuous print static nature sequence; Static gesture knows method for distinguishing to be had a lot, and in method, the method for template matches, the method based on machine learning of rule, dynamic gesture has Time and place change, becomes temporal symbol sebolic addressing after gesture feature quantization encoding;
(4) based on the complexion model parameter initialization method of sampling, by to being collected in portion's image pattern, auto-initiation hand skin color model, hand area and face area parameter, the gesture tracking method under blocking based on centroid estimation hand face is described at gesture tracking, at gesture feature Extraction parts, use the finger characteristic extracting method based on profile, and extract motion vector as dynamic gesture feature, it is finally gesture identification part, static gesture identification have employed the gesture identification strategy cooperated based on orthogonal camera, dynamic hand gesture recognition then uses a kind of method of overlapping independently hidden Markov model in conjunction with two,
(5) input is the gesture video of two cameras, output is the hand gesture location and gesture command that identify, hand gesture location is wherein for being converted into system cursor position, gesture motion is mapped as the keyboard shortcut of application software, by gesture input being converted into the input of the standard device of system, system uses two common cameras, the better noise of video quality of camera is fewer, process will become more accurate, system employs two cameras, one of them is arranged on above the screen of front, from forward observation gesture motion, another is arranged on bottom, observe gesture motion from below, bottom camera can according to specific environment condition, selection is arranged on top, subscriber station stands in screen front, just camera is put to front-facing camera and the end, with front-facing camera apart from about 0.2 meter, the below of the position when hand that bottom camera position is adjusted to user stretches out.
This system can identify the gesture command of occupant automatically, conveniently operates on it, and decreases the time of human pilot both hands departure direction dish, safety is with reliable more, corresponding instruction can be obtained, handled easily according to driver's finger movement, not affect the security of driving.
The above; it is only preferred embodiment of the present invention; not any pro forma restriction is done to the present invention, every according to technology of the present invention, method in fact to any simple modification, equivalent variations that above embodiment is done, all fall within protection scope of the present invention.
Claims (1)
1. vehicle intelligent gesture recognition system, is characterized in that, comprises the following steps:
(1) in gesture identification framework, gesture model is a meat and potatoes, according to different application backgrounds, the model adopted in gesture identification has difference, and for different gesture models, the gestures detection adopted and track algorithm, feature extraction, recognition technology also has difference, gesture modeling is mainly divided into based on apparent gesture model and the gesture model based on three-dimensional, a kind of two-dimentional modeling based on apparent gesture modeling, from the feature that the observable plane picture information of two dimensional surface is described in, the model based on color and two kinds, the model based on profile is mainly comprised based on apparent gesture model, gesture model based on color is set images of gestures being regarded as pixel color, by the feature of the color extracting hand, gesture is described, common feature based on the gesture model of color is color histogram, gesture model based on profile is that handle regards a profile as, by the geometric properties of the profile extracting hand in hand images, gesture is described,
(2) gestures detection is processing section foremost in gesture recognition process flow process with following the tracks of, its process gets images of gestures or sequence from camera, therefrom detect and dividing gesture object, if dynamic hand gesture recognition, opponent is also wanted to follow the tracks of, method based on movable information supposes to only have hand to be moving object in video, wherein a kind of method is background subtraction, it requires that stationary background is constant, the every frame in video and background subtracting, the part vanishing that background is identical, different parts just thinks the object moved, i.e. hand, another kind method is difference frame method, by present frame and former frame or front some frame subtract, detect that difference between two frames is to determine the initiation region of hand exercise, but the gesture motion amplitude between general consecutive frame is little, difference frame method can only detect the profile of motion, the hand that subtraction could correctly detect and segmentation is moved to be done with lower frame sampling rate or every some frames,
(3) extraction of gesture feature is closely-related with gesture model, different gesture models has difference to be had in gesture feature, static gesture identification is with dynamically also different in the feature of gesture identification, the feature of static gesture is the static information of the hand described, profile, area, dynamic gesture feature is continuous print static nature sequence; Static gesture knows method for distinguishing to be had a lot, and in method, the method for template matches, the method based on machine learning of rule, dynamic gesture has Time and place change, becomes temporal symbol sebolic addressing after gesture feature quantization encoding;
(4) based on the complexion model parameter initialization method of sampling, by to being collected in portion's image pattern, auto-initiation hand skin color model, hand area and face area parameter, the gesture tracking method under blocking based on centroid estimation hand face is described at gesture tracking, at gesture feature Extraction parts, use the finger characteristic extracting method based on profile, and extract motion vector as dynamic gesture feature, it is finally gesture identification part, static gesture identification have employed the gesture identification strategy cooperated based on orthogonal camera, dynamic hand gesture recognition then uses a kind of method of overlapping independently hidden Markov model in conjunction with two,
(5) input is the gesture video of two cameras, output is the hand gesture location and gesture command that identify, hand gesture location is wherein for being converted into system cursor position, gesture motion is mapped as the keyboard shortcut of application software, by gesture input being converted into the input of the standard device of system, system uses two common cameras, the better noise of video quality of camera is fewer, process will become more accurate, system employs two cameras, one of them is arranged on above the screen of front, from forward observation gesture motion, another is arranged on bottom, observe gesture motion from below, bottom camera can according to specific environment condition, selection is arranged on top, subscriber station stands in screen front, just camera is put to front-facing camera and the end, with front-facing camera distance 0.2 meter, the below of the position when hand that bottom camera position is adjusted to user stretches out.
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Cited By (12)
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| CN107608501A (en) * | 2016-07-11 | 2018-01-19 | 现代自动车株式会社 | User interface facilities and vehicle and the method for control vehicle including it |
| CN108181989A (en) * | 2017-12-29 | 2018-06-19 | 北京奇虎科技有限公司 | Gestural control method and device, computing device based on video data |
| CN108229345A (en) * | 2017-12-15 | 2018-06-29 | 吉利汽车研究院（宁波）有限公司 | A kind of driver's detecting system |
| CN109614953A (en) * | 2018-12-27 | 2019-04-12 | 华勤通讯技术有限公司 | A kind of control method based on image recognition, mobile unit and storage medium |
| CN109634415A (en) * | 2018-12-11 | 2019-04-16 | 哈尔滨拓博科技有限公司 | It is a kind of for controlling the gesture identification control method of analog quantity |
| CN109919107A (en) * | 2019-03-11 | 2019-06-21 | 青岛科技大学 | A kind of traffic police's gesture identification method and unmanned vehicle based on deep learning |
| CN110353622A (en) * | 2018-10-16 | 2019-10-22 | 武汉交通职业学院 | A kind of vision testing method and eyesight testing apparatus |
| CN111158491A (en) * | 2019-12-31 | 2020-05-15 | 苏州莱孚斯特电子科技有限公司 | Gesture recognition man-machine interaction method applied to vehicle-mounted HUD |
| CN111158457A (en) * | 2019-12-31 | 2020-05-15 | 苏州莱孚斯特电子科技有限公司 | Vehicle-mounted HUD (head Up display) human-computer interaction system based on gesture recognition |
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| CN113448429A (en) * | 2020-03-25 | 2021-09-28 | 南京人工智能高等研究院有限公司 | Method and device for controlling electronic equipment based on gestures, storage medium and electronic equipment |
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107608501A (en) * | 2016-07-11 | 2018-01-19 | 现代自动车株式会社 | User interface facilities and vehicle and the method for control vehicle including it |
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| CN108181989B (en) * | 2017-12-29 | 2020-11-20 | 北京奇虎科技有限公司 | Gesture control method and device based on video data and computing equipment |
| CN110353622A (en) * | 2018-10-16 | 2019-10-22 | 武汉交通职业学院 | A kind of vision testing method and eyesight testing apparatus |
| CN109634415B (en) * | 2018-12-11 | 2019-10-18 | 哈尔滨拓博科技有限公司 | It is a kind of for controlling the gesture identification control method of analog quantity |
| CN109634415A (en) * | 2018-12-11 | 2019-04-16 | 哈尔滨拓博科技有限公司 | It is a kind of for controlling the gesture identification control method of analog quantity |
| CN109614953A (en) * | 2018-12-27 | 2019-04-12 | 华勤通讯技术有限公司 | A kind of control method based on image recognition, mobile unit and storage medium |
| CN109919107A (en) * | 2019-03-11 | 2019-06-21 | 青岛科技大学 | A kind of traffic police's gesture identification method and unmanned vehicle based on deep learning |
| CN109919107B (en) * | 2019-03-11 | 2023-03-24 | 青岛科技大学 | Traffic police gesture recognition method based on deep learning and unmanned vehicle |
| CN111158491A (en) * | 2019-12-31 | 2020-05-15 | 苏州莱孚斯特电子科技有限公司 | Gesture recognition man-machine interaction method applied to vehicle-mounted HUD |
| CN111158457A (en) * | 2019-12-31 | 2020-05-15 | 苏州莱孚斯特电子科技有限公司 | Vehicle-mounted HUD (head Up display) human-computer interaction system based on gesture recognition |
| CN113448429A (en) * | 2020-03-25 | 2021-09-28 | 南京人工智能高等研究院有限公司 | Method and device for controlling electronic equipment based on gestures, storage medium and electronic equipment |
| CN111469859A (en) * | 2020-03-27 | 2020-07-31 | 一汽奔腾轿车有限公司 | Automobile gesture interaction system |
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