CN110443197A - A kind of visual scene intelligent Understanding method and system - Google Patents
A kind of visual scene intelligent Understanding method and system Download PDFInfo
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- CN110443197A CN110443197A CN201910719181.6A CN201910719181A CN110443197A CN 110443197 A CN110443197 A CN 110443197A CN 201910719181 A CN201910719181 A CN 201910719181A CN 110443197 A CN110443197 A CN 110443197A
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Abstract
The present invention relates to a kind of visual scene understanding methods, comprising the following steps: S10: acquisition current scene data；S20: current scene information is handled using deep learning method and traditional images analytical, to obtain prospect, the background and context feature of current scene；S30: using the contextual data of deep learning method study different scenes, the case where to judge current scene and/or it is expected that the case where will occur and counte-rplan are formulated.The application combines traditional image analysis method with deep learning method to obtain the prospect of scene, background and context parameter, and the environmental parameter variation for passing through study different scenes, come the case where judging current scene or it is expected that the case where will occur and the optional processing scheme of scenario that reply will occur, when scene is unfavorable for user preference, user can make counter-measure by optional processing scheme.
Description
Technical field
The present invention relates to technical field of vision detection, a kind of particularly visual scene intelligent Understanding method and system.
Background technique
In the method that existing visual scene understands, it is only concerned about target detection, scene cut and the mesh of scene
Mark tracking etc., user can understand the location of target and morphological feature by the judgement to target, but cannot
Data acquisition is carried out to the actual foreground of current environment, background parameter and is shown, it will can to user or cannot remind
The situation etc. that can occur, is unfavorable for user according to current context information to judge, to make as early as possible according to current scene
Prevent counter-measure.
Summary of the invention
To solve the above-mentioned problems, above-mentioned for solving the present invention provides a kind of employee's adjustmenting management method and system
Problem.
In a first aspect, the application provides a kind of visual scene intelligent Understanding system, comprising:
Data acquisition module is used to acquire prospect, the background and context supplemental characteristic of current scene；
Data memory module is used to store the collected data of the data acquisition module institute；
Visual processes analysis module comprising intelligence learning module and image processing module, described image processing module benefit
The collected data of institute are analyzed and processed with traditional images processing method, the intelligence learning module utilizes deep learning side
Method is analyzed and processed the collected data of institute, to obtain prospect, the background and context feature of current scene；
And the intelligence learning module is by the contextual data of study different scenes, the case where to judge current scene and/
Or it is expected that the case where will occur and formulate counte-rplan.
In one embodiment, the intelligence learning module is in such a way that convolution, pondization calculate, and utilizes the depth
Trained model is analyzed and processed data in degree learning method.
In one embodiment, the trained model is using deep neural network structure to comprising a large amount of positive and negative
The data set of sample carries out what model learning training obtained.
In one embodiment, the contextual data other than data collecting module collected to the locating scene of user's habit
When, the intelligence learning module to contextual data other than the locating scene of collected user habit analyze, if symbol
The locating scene characteristic of family habit is shared, then is used as positive sample, is otherwise used as negative sample.
In one embodiment, the visual scene intelligent Understanding system further includes communication module, the communication module
For with user terminal communication, by data collected, and the result and counte-rplan of judgement are sent to user.
In one embodiment, described image processing and visual analysis identification module using Threshold segmentation, Color Picking,
The method that luminance contrast and canny operator contours extract and image area calculate carries out at analysis data collected
Reason.
In one embodiment, the deep learning method includes RCNN target detection and FCN foreground segmentation.
In one embodiment, the data acquisition module includes photosensitive sensor, sound transducer, temperature and humidity sensing
Device and image capture module,
The photosensitive sensor is used to acquire the luminance contrast data of scene；
The sound transducer is for acquiring voice data；
The Temperature Humidity Sensor is for acquiring data of the Temperature and Humidity module；
Described image acquisition module is used to acquire people, object, the number of event, position and the categorical data in scene image.
Second aspect, this application provides a kind of visual scene intelligent Understanding methods, comprising the following steps:
S10: prospect, the background and context supplemental characteristic of current scene are acquired；
S20: handling current scene data using deep learning method and traditional images analytical, current to obtain
Prospect, the background and context feature of scene；
S30: using the contextual data of deep learning method study different scenes, the case where to judge current scene and/or
It is expected that the case where will occurring and counte-rplan are formulated.
In one embodiment, in step S30: the case where judging current scene includes:
Scene foreground target positioning and detection, scene background information extraction, three-dimensional space present case or it is expected that be
It will be the case where appearance and the optional processing scheme of scenario that will occur of reply.
In one embodiment, further include step S40 after step S30: the estimate of situation of current scene will be made
It is stored for trained model and is used for next deep learning.
Compared with the prior art, the advantages of the present invention are as follows: the application by traditional image analysis method and deep learning
Method combines to obtain the prospect of scene, background and context parameter, and by the environmental parameter variation of study different scenes, comes
The case where judging current scene or it is expected that the case where will occur and scenario that reply will occur optionally is handled
Scheme is conducive to user and is effectively treated, prevents to the scenario currently faced, when scene is unfavorable for user preference, uses
Family can make counter-measure by optional processing scheme.
Detailed description of the invention
The invention will be described in more detail below based on embodiments and refering to the accompanying drawings.
Fig. 1 is the schematic diagram according to a kind of visual scene intelligent Understanding system of the application.
Fig. 2 is the flow chart one according to a kind of visual scene intelligent Understanding method of the application.
Fig. 3 is the flowchart 2 according to a kind of visual scene intelligent Understanding method of the application.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings.
As shown in Figure 1, it is shown that a kind of visual scene intelligent Understanding system according to the present invention, including data acquisition module
Block, data memory module and visual processes analysis module.
Wherein, data acquisition module is used to acquire prospect, the background and context supplemental characteristic of current scene.Data store mould
Block collected data of acquisition module institute for storing data.Visual processes analysis module includes intelligence learning module and image again
Processing module, image processing module are analyzed and processed the collected data of institute using traditional images processing method, and intelligence is learned
It practises module and the collected data of institute is analyzed and processed using deep learning method, to obtain prospect, the background of current scene
And environmental characteristic.
In addition, intelligence learning module is also used to learn the scenario parameters of different scenes, the case where to judge current scene and/
Or it is expected that the case where will occur and formulate counte-rplan.
Wherein, data acquisition module includes photosensitive sensor, sound transducer, Temperature Humidity Sensor and Image Acquisition mould
Block.Photosensitive sensor is used to acquire the luminance contrast data of scene；Sound transducer is for acquiring voice data；Temperature and humidity passes
Sensor is for acquiring data of the Temperature and Humidity module；Image capture module is for acquiring the people in scene image, object, the number of event, position
And categorical data.Image capture module can be high-definition camera, high speed camera etc..
Wherein, traditional images processing method includes Threshold segmentation, Color Picking, luminance contrast and canny operator profile
Extraction and image area calculating etc..Deep learning method includes RCNN target detection and FCN foreground segmentation etc..
Wherein, intelligence learning module can be in such a way that convolution, pondization calculate, and utilize training in deep learning method
Good model is analyzed and processed data.Trained model can be using deep neural network structure to comprising largely just
The data set of negative sample carries out what model learning training obtained.In the data set, it is accustomed in data collecting module collected to user
When contextual data other than locating scene, intelligence learning module to field other than the locating scene of collected user's habit
Scape data are analyzed, if meeting the locating scene characteristic of user's habit, are used as positive sample, are otherwise used as negative sample.
In a preferred embodiment, visual scene intelligent Understanding system further includes communication module, communication module be used for
User terminal communication, by data collected, and judgement current scene the case where and optional counte-rplan be sent to use
Family.
Fig. 2 shows a kind of visual scene intelligent Understanding method according to the application, comprising the following steps:
Step 1: acquisition current scene data.
Using the luminance contrast data of photosensitive sensor acquisition scene, voice data, benefit are acquired using sound transducer
With Temperature Humidity Sensor acquire data of the Temperature and Humidity module, using image capture module acquisition scene image in people, object, event
Number, position and categorical data.
Step 2: current scene information is handled using deep learning method and traditional images analytical, to obtain
Prospect, the background and context feature of current scene.
Step 3: using the contextual data of deep learning method study different scenes, the case where to judge current scene, or
It is expected that the case where will occurring and counte-rplan are formulated.
Wherein, the case where judging current scene include: scene foreground target positioning and detection, scene background information extraction,
Three-dimensional space present case or it is expected that the case where will occur and scenario that reply will occur optionally is handled
Scheme.
It in a preferred embodiment, further include step 4: by point of prospect, background and context feature to current scene
Analysis processing result is stored in data memory module as trained model and uses for next deep learning.
In conclusion the application is complicated to the luminance contrast of current scene, color, foreground target, background by being added
The acquisition module of the environmental parameters such as degree, so that further comprising the background information of current scene when the target prospect of detection current scene
Deng, traditional image analysis method is combined with deep learning method to obtain the prospect of scene, background and context parameter, and
By learning the environmental parameter variation of different scenes, the case where to judge current scene or it is expected that the case where will occur and
The optional processing scheme of scenario that will occur is coped with, when scene is unfavorable for user preference, user can be by optional
Processing scheme make counter-measure.
It is below the embodiment that the visual scene understanding method is applied to industrial production line scene, specifically:
Contextual data acquisition, the contextual data that need to be acquired are carried out by photosensitive sensor, high-definition camera, high speed camera etc.
Including essential informations such as the employee, product, the processes in production line that include in scene brightness contrast, temperature and humidity, scene image
Data save the data in data memory module after acquisition.
Data memory module sends the data to visual processes analysis module, visual processes can be analyzed in image at
Reason module and intelligence learning module are configured to an entirety in order to package application.By by the traditional analysis of image processing module
Method (Threshold segmentation, Color Picking, luminance contrast, canny operator contours extract, image area calculate) and intelligence learning mould
The deep learning method (RCNN target detection, FCN foreground segmentation) of block matches, and calls trained in deep learning method
Model is analyzed and processed current scene, obtains work employee, the product produced, production line produced of current scene
In the data such as process.
Intelligence learning module, according to the feature of industrial scene information, is matched to above-mentioned scene in learning model as industrial field
Scape, and correspondence classifies current scene, and labeled as industrial scene, corresponding task dressing, behavioural characteristic are then labeled as
Product line worker, corresponding article characteristics are then labeled as production material or product etc..To excavate effective industrial scene characteristic,
Biological characteristic, object category feature, and modeling recovery is carried out to current scene, it is fitted properly with 3-D image or in conjunction with audio
Current scene environment be presented to the user, so that user does counte-rplan to current scene situation, such as: current industrial scene
In, somewhere temperature is higher and brightness is higher, excludes except production technology demand, judges that it, with the presence or absence of fire hazard, is mentioned in time
Awake user does the counter-measure for excluding fire hazard, and provides nearest fire extinguisher, alarm, fire hydrant and exit passageway etc. and mentioned
It wakes up.
It is to be appreciated that the visual scene understanding method of the application applies also for the detection of road conditions traffic scene, periphery
Environment food, clothing, housing and transportation, gas station or detection of scenic spot etc., to show the actual conditions of current scene.
Although by reference to preferred embodiment, invention has been described, the case where not departing from the scope of the present invention
Under, various improvement can be carried out to it and can replace component therein with equivalent.Especially, as long as there is no structures to rush
Prominent, items technical characteristic mentioned in the various embodiments can be combined in any way.The invention is not limited to texts
Disclosed in specific embodiment, but include all technical solutions falling within the scope of the claims.
Claims (11)
1. a kind of visual scene intelligent Understanding system characterized by comprising
Data acquisition module is used to acquire prospect, the background and context supplemental characteristic of current scene；
Data memory module is used to store the collected data of the data acquisition module institute；
Visual processes analysis module comprising intelligence learning module and image processing module, described image processing module utilize biography
System image processing method is analyzed and processed the collected data of institute, and the intelligence learning module utilizes deep learning method pair
The collected data of institute are analyzed and processed, to obtain prospect, the background and context feature of current scene；
And the intelligence learning module is by the contextual data of study different scenes, it is the case where to judge current scene and/or pre-
The case where meter will occur simultaneously formulates counte-rplan.
2. visual scene intelligent Understanding system according to claim 1, which is characterized in that the intelligence learning module uses
The mode that convolution, pondization calculate, and data are analyzed and processed using trained model in the deep learning method.
3. visual scene intelligent Understanding system according to claim 2, which is characterized in that the trained model is to adopt
What model learning training obtained is carried out to the data set comprising a large amount of positive negative samples with deep neural network structure.
4. visual scene intelligent Understanding system according to claim 3, which is characterized in that arrived in data collecting module collected
When contextual data other than the locating scene of user's habit, the intelligence learning module is to locating for institute's collected user habit
Contextual data other than scene is analyzed, if meeting the locating scene characteristic of user's habit, is used as positive sample, otherwise conduct
Negative sample.
5. visual scene intelligent Understanding system according to claim 1, which is characterized in that the visual scene intelligent Understanding
System further includes communication module, and the communication module is used for and user terminal communication, by data collected, and the result of judgement
And counte-rplan are sent to user.
6. visual scene intelligent Understanding system according to claim 1, which is characterized in that described image processing and vision point
It analyses identification module and uses Threshold segmentation, Color Picking, luminance contrast and canny operator contours extract and image area meter
The method of calculation is analyzed and processed data collected.
7. visual scene intelligent Understanding system according to claim 1, which is characterized in that the deep learning method includes
RCNN target detection and FCN foreground segmentation.
8. visual scene intelligent Understanding system according to claim 1, which is characterized in that the data acquisition module includes
Photosensitive sensor, sound transducer, Temperature Humidity Sensor and image capture module,
The photosensitive sensor is used to acquire the luminance contrast data of scene；
The sound transducer is for acquiring voice data；
The Temperature Humidity Sensor is for acquiring data of the Temperature and Humidity module；
Described image acquisition module is used to acquire people, object, the number of event, position and the categorical data in scene image.
9. a kind of visual scene intelligent Understanding method, which comprises the following steps:
S10: prospect, the background and context supplemental characteristic of current scene are acquired；
S20: current scene data are handled using deep learning method and traditional images analytical, to obtain current scene
Prospect, background and context feature；
S30: using the contextual data of deep learning method study different scenes, the case where to judge current scene and/or it is expected that
The case where will occurring, simultaneously formulates counte-rplan.
10. visual scene intelligent Understanding method according to claim 9, which is characterized in that in step S30: judgement is current
The case where scene includes:
The positioning of scene foreground target and detection, three-dimensional space present case or it is expected that will go out at scene background information extraction
The optional processing scheme of scenario that existing situation and reply will occur.
11. visual scene intelligent Understanding method according to claim 9, which is characterized in that after step S30, also wrap
It includes step S40: the estimate of situation to current scene being stored for next deep learning as trained model and is used.
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Application publication date: 20191112 |