CN108805073A - A kind of safety monitoring dynamic object optimization track lock method and system - Google Patents
A kind of safety monitoring dynamic object optimization track lock method and system Download PDFInfo
- Publication number
- CN108805073A CN108805073A CN201810575819.9A CN201810575819A CN108805073A CN 108805073 A CN108805073 A CN 108805073A CN 201810575819 A CN201810575819 A CN 201810575819A CN 108805073 A CN108805073 A CN 108805073A
- Authority
- CN
- China
- Prior art keywords
- image
- target
- module
- video
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Classifications
-
- 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
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
- G06V20/42—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items of sport video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant recognition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/66—Remote control of cameras or camera parts, e.g. by remote control devices
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Signal Processing (AREA)
- Human Computer Interaction (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Closed-Circuit Television Systems (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of safety monitoring dynamic object optimize track lock method and system, method includes that acquisition video data, target are confined, foreground extraction target identification, adjusting parameter are determining, video simplifies compression；System includes that video acquisition module, memory module, personal target confine processing module, foreground extracting module, data modeling module, pixel classifications module, personal target generation module, location position module, adjusting parameter generation module, video data compression processing module and communication module.The present invention can accurately extract the personal target in video image, and according to the acquisition angles of the mobile synchronous adjustment video of target and direction, the image for collecting personal target of moment complete display, both the accurate and effective that data volume in transmission of video in turn ensures personal target information had been reduced, there is higher practical value and has been widely applied foreground.
Description
Technical field
The present invention relates to field of intelligent control technology, and in particular to a kind of safety monitoring dynamic object optimization track lock side
Method and system.
Background technology
With the rapid development of social economy, scientific and technological rapid advances, people's lives mode have occurred that change, far
Journey video, video surveillance applications it is increasingly wider.Such as long-distance education, it is long-range anywhere, so that it may freely to select
It needs the content learnt to be learnt, is installed on the more important field of the public safeties such as commercial square, supermarket and school for another example
The video monitoring system of conjunction.
And have more problem for the extraction of personal target during existing video analysis, for example how effectively to determine
How personal target eliminates influence of the dynamic background in foreground extraction, how to mitigate influence of the target shadow to Objective extraction
And if reducing noise, usually common frame difference method is relatively specific for moving target and the gray scale difference of background is more, and
The Computer Vision that background varies less, can not extensive use, other methods also can not effectively solve problem above.
On the other hand, there is also adjust shooting angle based on the coarse localization position of target in video in the prior art
Direct-seeding, but the Adjustment precision of this mode is inadequate, is easy to influence shooting picture bad student during adjustment, shooting figure
As may be smudgy, and cannot be satisfied the excessive situation of big visual field and goal activities section, it may appear that larger blind area or
By recording acquisition at a distance, reality is likely to occur when watching can not accurately track personal target.
In addition in the prior art, video is transmitted by network because being influenced by factors such as network bandwidths, very
The phenomenon that being susceptible to off and on influences the effect played.Which kind of mode is either used, to obtain the people of clear and smooth
The video display effect of body target is required for the stream medium data generated in dynamic to long-distance video to be effectively compressed.But it is existing
There is technology still without providing to personal target targetedly video compress solution.
Invention content
In view of the deficiencies of the prior art, a kind of safety monitoring dynamic object of present invention offer optimizes track lock method and is
System, can accurately extract the personal target in video image, and the acquisition angles of the mobile synchronous adjustment video according to target
And direction, the image for collecting personal target of moment complete display, it had both reduced data volume in transmission of video and has in turn ensured people
The accurate and effective of body target information has higher practical value and is widely applied foreground.
The present invention solves technical problem and adopts the following technical scheme that：
The present invention provides a kind of safety monitoring dynamic objects to optimize track lock method, includes the following steps：
S1, Security Personnel, which authorize, to be logged in, wake-up safety monitoring service, the infrared letter of the person in first cycle detection ambient enviroment
Number,
Following operation is taken after detecting personal infrared signal：
S11, detection video camera apparatus, if detecting video camera apparatus, automatic start is transferred to step S12, if not examining
Video camera apparatus is measured, then sends a warning message and Security Personnel is reminded correctly to install, until being successfully detected；
S12, video data is acquired in real time, video data is separated into image data and voice data to be deposited sequentially in time
Storage etc. is pending；
S13, it takes image data pretreatment measure mainly and includes：Target shadow pair is alleviated using HSV color spaces
The influence of Objective extraction eliminates salt-pepper noise using median filter, so that image is become simple using image binaryzation, uses
Picture noise and filling image cavity are eliminated in extraction respectively for corrosion and expansion in iconology denoising；
S2, each frame image data of the video data of step S1 acquisitions is confined using Faster RCNN progress target,
Suggestion box is generated, it is pending according to prefixed time interval T stage extractions storage etc. that the image data in Suggestion box will be generated；
Image data mixed Gauss model in S3, the generation Suggestion box for storing stage extraction in step S2 carries out foreground
Extraction identification obtains personal target data, including：
S31, any pixel point in the preceding M frames image in this section of image data is modeled to obtain the probability of the pixel
Distribution function：
X indicates the pixel gray value, and at the time of t is that the frame image corresponds to, K is the number for the Gauss model for choosing fitting
η is Gaussian probability-density function, and ω belongs to the weight of different functions, μ and ε be respectively n-th of Gauss model of t moment mean value to
Amount and covariance matrix；
S32, probability-distribution function P (X are being obtainedt) after, since M+1 frames, after being obtained to each frame image data more
New mixed Gauss model is matched with each pixel in present image with mixed Gauss model, if matching, then it is assumed that the picture
Vegetarian refreshments is background pixel；Otherwise foreground pixel data are extracted as；
S33, the personal number of targets in each frame image is obtained according to the foreground pixel data combined treatment extracted in S32
According to；
S4, rectangular coordinate system is established as origin using the central point of every frame image, obtains the position of personal target in step S3
Parameter obtains the running orbit of personal target according to the difference of location parameter between consecutive frame image；
S5, according to the running orbit of personal target, obtain including adjustment time interval, angle information, elevation information, movement
Adjusting parameter including velocity information adjusts the angle and direction of video acquisition to ensure the person according to obtained adjusting parameter
Target is steadily restored at image center position；
S6, the video data of acquisition is transmitted to after handling squeeze operation to server storage user is waited for retrieve for examination, institute
Processing squeeze operation is stated to include the following steps：
S61, the personal target handled according to step 4 location parameter according to preset frame number Q, calculate adjacent Q
Personal target location variation difference △ S between frame image；
S62, when target location variation difference △ S be more than predetermined threshold value Y when, extremely by adjacent Q frames image whole compression transmission
Server；
When target location, variation difference △ S are less than predetermined threshold value Y, adjacent Q frames image is removed according to every a frame
One frame image, the video image compression for obtaining scaled-down version are sent into server storage.
Preferably, include Kalman filtering and Morphological scale-space to the processing of foreground pixel data in the step S33.
Preferably, the step S5 is further comprising the steps of：
If adjusting parameter is more than predetermined threshold, the angle and direction of video acquisition is adjusted by max-thresholds, is simultaneously emitted by
Abnormal alarm information, closes the road video transfer signal, and access includes the another road video transfer signal of personal target.
Preferably, the distance in S4 representated by the per unit of rectangular coordinate system is according to image in advance calibrated visual field
Minimum range representated by minimum resolution converts to obtain.
The present invention also provides a kind of safety monitoring dynamic objects to optimize track lock system, including：
Login module authorizes for providing user's logentry and wakes up safety monitoring dynamic object optimization track lock system
System；
Video acquisition module, including multi-channel video capturing complexes, for acquiring video data in real time, by video data
It is separated into image data and voice data and stores sequentially in time etc. pending, and adopted per road video according to adjustment signal adjustment
The angle and direction of collection；
Personal detection module, for personal infrared signal in cycle detection ambient enviroment, when detecting personal infrared signal
After trigger video acquisition module；
Memory module acquires video data in real time for storing video acquisition；
Personal target confines processing module, the video data for extracting memory module storage, and by each frame picture number
It is confined according to using Faster RCNN to carry out target, generates Suggestion box, the image data in Suggestion box will be generated according to preset time
It is pending to be spaced T stage extractions storage etc.；
Foreground extracting module is carried out for the image data mixed Gauss model in the generation Suggestion box of stage extraction storage
Foreground extraction identifies to obtain personal target data, including：
Data modeling module, for being modeled to obtain to any pixel point in the preceding M frames image in this section of image data
The probability-distribution function of the pixel：
X indicates the pixel gray value, and at the time of t is that the frame image corresponds to, K is the number for the Gauss model for choosing fitting
η is Gaussian probability-density function, and ω belongs to the weight of different functions, μ and ε be respectively n-th of Gauss model of t moment mean value to
Amount and covariance matrix；
Pixel classifications module, for obtaining probability-distribution function P (Xt) after, since M+1 frames, to each frame image
Mixed Gauss model is updated after data acquisition, is matched with mixed Gauss model with each pixel in present image, if phase
Match, then it is assumed that the pixel is background pixel；Otherwise foreground pixel data are extracted as；
Personal target generation module, for being obtained in each frame image according to the foreground pixel data combined treatment of extraction
Personal target data；
Location position module obtains personal target for establishing rectangular coordinate system as origin using the central point of every frame image
Location parameter, the running orbit of personal target is obtained according to the difference of location parameter between consecutive frame image；
Adjusting parameter generation module obtains including adjustment time interval, angle for the running orbit according to personal target
Adjusting parameter including information, elevation information, motion velocity information, and adjusting parameter is fed back into video acquisition module, video
Acquisition module adjusts the angle and direction of video acquisition to ensure that personal target is steadily restored to according to obtained adjusting parameter
At image center position；
Video data compression processing module, for according to the obtained location parameter of personal target according to preset frame number Q,
Calculate the personal target location variation difference △ S between adjacent Q frames image；When target location, variation difference △ S are more than default threshold
When value Y, by adjacent Q frames image whole compression transmission to server；When target location, variation difference △ S are less than predetermined threshold value Y
When, adjacent Q frames image is removed into a frame image according to every a frame, obtains the vedio data of scaled-down version and compression；
Communication module, for the video data transmission that acquires video acquisition module to memory module, video counts will be passed through
It is sent into server storage according to compressing processing module treated vedio data and adjusting parameter is transmitted to video acquisition
Module.
Preferably, the personal target generation module further includes Kalman filtering module and Morphological scale-space module.
Preferably, further include abnormal parameters processing module in the adjusting parameter generation module, for surpassing when adjusting parameter
When crossing predetermined threshold, command signal is sent out to video acquisition module, video acquisition module is by max-thresholds adjustment video acquisition
Angle and direction is simultaneously emitted by abnormal alarm information, closes the road video transfer signal, and another road of the access comprising personal target regards
Keep pouring in defeated signal.
Preferably, further include image pre-processing module, for being carried out before target is confined also to every using Faster RCNN
One frame image is pre-processed, including：Influence of the target shadow to Objective extraction is alleviated using HSV color spaces, is used
Median filter eliminates salt-pepper noise, so that image is become simple using image binaryzation, using the corrosion in iconology denoising
Picture noise and filling image cavity are eliminated in extraction respectively with expansion.
Preferably, the video acquisition complexes include photographic device and adjusting apparatus；
The photographic device includes main photographic device and second camera device, and the second camera device is two, respectively
There is 50% visual field that is superimposed with main photographic device, without superimposition between the second camera device；
The adjusting apparatus is used to adjust the angle of the video acquisition of photographic device according to the adjusting parameter that communication module transmits
Degree and direction.
Preferably, the size of the number K values of the Gauss model for choosing fitting is 4.
Compared with prior art, the present invention has following advantageous effect：
(1) a kind of safety monitoring dynamic object of the invention optimizes track lock method by first carrying out mesh to image data
Mark is confined, then carries out foreground extraction using mixed Gauss model, and then accurately obtains personal mesh target area, the use of innovation this
Kind combination extraction pattern can pick out moving target from the background of differing complexity, additionally it is possible to effectively avoid due to taking the photograph
Being shaken as head causes background variation violent, and efficiently separates background, so as to complete track and identify Deng follow-up works.
(2) a kind of safety monitoring dynamic object of the invention optimization track lock method by the personal target to extraction into
Row establishes the position positioning of coordinate system formula, can automatic tracing person target, need not manually adjust, you can accurate in real time pure and fresh
The video image information for taking personal target, and the adjustment photographic device that can continue so that user retrieves for examination more clear
Chu, experience is more preferably.
(3) a kind of safety monitoring dynamic object of the invention optimization track lock method passes through data compression process, innovation
Use according to the position of personal target move degree, carry out simplifying frame number, can fully be opened up during ensureing remote transmission
While body target information of leting others have a look at, the transmission quantity of teledata is effectively reduced, to effectively reduce the cost for carrying out remote monitoring.
(4) a kind of safety monitoring dynamic object of the invention optimizes track lock method, defeated by being positioned to personal target
Kalman filtering and Morphological scale-space have been carried out before going out, and are effectively reduced the influence of noise light photograph, are improved target and image
Signal-to-noise ratio；Influence of the target shadow to Objective extraction is alleviated using HSV color spaces in image preprocessing simultaneously, is used
Median filter eliminates salt-pepper noise, so that image is become simple using image binaryzation, using the corrosion in iconology denoising
Picture noise and filling image cavity are eliminated in extraction respectively with expansion.
(5) a kind of safety monitoring dynamic object of the invention optimization track lock system provides a kind of convenient, fast, high
Effect, low cost operation personal target lock-on with mode, system flexibility, the degree of automation are higher, and recognition speed is fast, accuracy rate
Height, at the same also have the advantages that it is versatile, open it is strong, autgmentability is strong.
(6) a kind of safety monitoring dynamic object of the invention optimization track lock system can accurately extract video image
In personal target, and according to the acquisition angles of the mobile synchronous adjustment video of target and direction, the acquisition of moment complete display
To the image of personal target, the accurate and effective that data volume in transmission of video in turn ensures personal target information had both been reduced, had been had
There is higher practical value and is widely applied foreground.
Description of the drawings
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon：
Fig. 1 is that a kind of safety monitoring dynamic object of the present invention optimizes track lock method flow diagram；
Fig. 2 is that a kind of safety monitoring dynamic object of the present invention optimizes the particular flow sheet of track lock method and step S1；
Fig. 3 is that a kind of safety monitoring dynamic object of the present invention optimizes the foreground extraction target identification step of track lock method
Rapid particular flow sheet；
Fig. 4 is that a kind of video of safety monitoring dynamic object optimization track lock method of the present invention simplifies compression step
Particular flow sheet；
Fig. 5 is that a kind of safety monitoring dynamic object of the present invention optimizes the image preprocessing detailed process of track lock method
Figure；
Fig. 6 is that a kind of safety monitoring dynamic object of the present invention optimizes the structural schematic diagram of track lock system.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In addition, those skilled in the art will appreciate that, various aspects of the invention can be implemented as system, method or
Computer program product.Therefore, various aspects of the invention can be implemented as the form of software and hardware combining, can unite here
Referred to as circuit, " module " or " system ".In addition, in some embodiments, various aspects of the invention are also implemented as
The form of computer program product in one or more microprocessors readable medium, comprising micro- in the microprocessor readable medium
The readable program code of processor.
Below with reference to according to the method for the embodiment of the present invention, the flow chart of device (system) and computer program product
And/or the block diagram description present invention.It should be appreciated that each in each box and flowchart and or block diagram of flowchart and or block diagram
The combination of box can be realized by computer program instructions.These computer program instructions can be supplied to all-purpose computer,
The processor of special purpose computer or other programmable data processing units, to produce a kind of machine so that these computers
Program instruction when executed by a processor of a computer or other programmable data processing device, produces implementation flow chart
And/or the device of function action specified in one or more of block diagram box.It can also be these computer program instructions
Storage in computer-readable medium, these instruct so that computer, other programmable data processing units or other equipment with
Ad hoc fashion works, to which the instruction stored in computer-readable medium is just produced including implementation flow chart and/or block diagram
One or more of the instruction of function action specified in box manufacture (article of manufacture).?
Computer program instructions can be loaded on computer, other programmable data processing units or miscellaneous equipment make it is a series of
Operating procedure is executed on computer, other programmable devices or miscellaneous equipment, is formed computer and is realized process, so as to
The instruction executed on a computer or other programmable device, which provides, to be realized in one or more of flowchart and or block diagram side
The process of function action specified in frame.
As shown in figures 1 to 6, a kind of safety monitoring dynamic object of the present embodiment optimizes track lock method, including following step
Suddenly：
S1, Security Personnel, which authorize, to be logged in, wake-up safety monitoring service, the infrared letter of the person in first cycle detection ambient enviroment
Number,
Following operation is taken after detecting personal infrared signal：
S11, detection video camera apparatus, if detecting video camera apparatus, automatic start is transferred to step S12, if not examining
Video camera apparatus is measured, then sends a warning message and Security Personnel is reminded correctly to install, until being successfully detected；
S12, video data is acquired in real time, video data is separated into image data and voice data to be deposited sequentially in time
Storage etc. is pending；
S13, it takes image data pretreatment measure mainly and includes：Target shadow pair is alleviated using HSV color spaces
The influence of Objective extraction eliminates salt-pepper noise using median filter, so that image is become simple using image binaryzation, uses
Picture noise and filling image cavity are eliminated in extraction respectively for corrosion and expansion in iconology denoising；
S2, each frame image data of the video data of step S1 acquisitions is confined using Faster RCNN progress target,
Suggestion box is generated, it is pending according to prefixed time interval T stage extractions storage etc. that the image data in Suggestion box will be generated；
Image data mixed Gauss model in S3, the generation Suggestion box for storing stage extraction in step S2 carries out foreground
Extraction identification obtains personal target data, including：
S31, any pixel point in the preceding M frames image in this section of image data is modeled to obtain the probability of the pixel
Distribution function：
X indicates the pixel gray value, and at the time of t is that the frame image corresponds to, K is the number for the Gauss model for choosing fitting
η is Gaussian probability-density function, and ω belongs to the weight of different functions, μ and ε be respectively n-th of Gauss model of t moment mean value to
Amount and covariance matrix；
S32, probability-distribution function P (X are being obtainedt) after, since M+1 frames, after being obtained to each frame image data more
New mixed Gauss model is matched with each pixel in present image with mixed Gauss model, if matching, then it is assumed that the picture
Vegetarian refreshments is background pixel；Otherwise foreground pixel data are extracted as；
S33, the personal number of targets in each frame image is obtained according to the foreground pixel data combined treatment extracted in S32
According to；
S4, rectangular coordinate system is established as origin using the central point of every frame image, obtains the position of personal target in step S3
Parameter obtains the running orbit of personal target according to the difference of location parameter between consecutive frame image；
S5, according to the running orbit of personal target, obtain including adjustment time interval, angle information, elevation information, movement
Adjusting parameter including velocity information adjusts the angle and direction of video acquisition to ensure the person according to obtained adjusting parameter
Target is steadily restored at image center position；
S6, the video data of acquisition is transmitted to after handling squeeze operation to server storage user is waited for retrieve for examination, institute
Processing squeeze operation is stated to include the following steps：
S61, the personal target handled according to step 4 location parameter according to preset frame number Q, calculate adjacent Q
Personal target location variation difference △ S between frame image；
S62, when target location variation difference △ S be more than predetermined threshold value Y when, extremely by adjacent Q frames image whole compression transmission
Server；
When target location, variation difference △ S are less than predetermined threshold value Y, adjacent Q frames image is removed according to every a frame
One frame image, the video image compression for obtaining scaled-down version are sent into server storage.
Include Kalman filtering and Morphological scale-space to the processing of foreground pixel data in the present embodiment step S33.
The present embodiment step S5 is further comprising the steps of：
If adjusting parameter is more than predetermined threshold, the angle and direction of video acquisition is adjusted by max-thresholds, is simultaneously emitted by
Abnormal alarm information, closes the road video transfer signal, and access includes the another road video transfer signal of personal target.
Distance in the present embodiment step S4 representated by the per unit of rectangular coordinate system is according in advance calibrated visual field
Minimum range representated by interior image minimum resolution converts to obtain.
A kind of safety monitoring dynamic object in the present embodiment optimizes track lock system, including：
Login module authorizes for providing user's logentry and wakes up safety monitoring dynamic object optimization track lock system
System；
Video acquisition module, including multi-channel video capturing complexes, for acquiring video data in real time, by video data
It is separated into image data and voice data and stores sequentially in time etc. pending, and adopted per road video according to adjustment signal adjustment
The angle and direction of collection；
Personal detection module, for personal infrared signal in cycle detection ambient enviroment, when detecting personal infrared signal
After trigger video acquisition module；
Memory module acquires video data in real time for storing video acquisition；
Personal target confines processing module, the video data for extracting memory module storage, and by each frame picture number
It is confined according to using Faster RCNN to carry out target, generates Suggestion box, the image data in Suggestion box will be generated according to preset time
It is pending to be spaced T stage extractions storage etc.；
Foreground extracting module is carried out for the image data mixed Gauss model in the generation Suggestion box of stage extraction storage
Foreground extraction identifies to obtain personal target data, including：
Data modeling module, for being modeled to obtain to any pixel point in the preceding M frames image in this section of image data
The probability-distribution function of the pixel：
X indicates the pixel gray value, and at the time of t is that the frame image corresponds to, K is for the Gauss model for choosing fitting
Number, η is Gaussian probability-density function, and ω belongs to the weight of different functions, and μ and ε are the equal of n-th of Gauss model of t moment respectively
It is worth vector sum covariance matrix；
Pixel classifications module, for obtaining probability-distribution function P (Xt) after, since M+1 frames, to each frame image
Mixed Gauss model is updated after data acquisition, is matched with mixed Gauss model with each pixel in present image, if phase
Match, then it is assumed that the pixel is background pixel；Otherwise foreground pixel data are extracted as；
Personal target generation module, for being obtained in each frame image according to the foreground pixel data combined treatment of extraction
Personal target data；
Location position module obtains personal target for establishing rectangular coordinate system as origin using the central point of every frame image
Location parameter, the running orbit of personal target is obtained according to the difference of location parameter between consecutive frame image；
Adjusting parameter generation module obtains including adjustment time interval, angle for the running orbit according to personal target
Adjusting parameter including information, elevation information, motion velocity information, and adjusting parameter is fed back into video acquisition module, video
Acquisition module adjusts the angle and direction of video acquisition to ensure that personal target is steadily restored to according to obtained adjusting parameter
At image center position；
Video data compression processing module, for according to the obtained location parameter of personal target according to preset frame number Q,
Calculate the personal target location variation difference △ S between adjacent Q frames image；When target location, variation difference △ S are more than default threshold
When value Y, by adjacent Q frames image whole compression transmission to server；When target location, variation difference △ S are less than predetermined threshold value Y
When, adjacent Q frames image is removed into a frame image according to every a frame, obtains the vedio data of scaled-down version and compression；
Communication module, for the video data transmission that acquires video acquisition module to memory module, video counts will be passed through
It is sent into server storage according to compressing processing module treated vedio data and adjusting parameter is transmitted to video acquisition
Module.
Personal target generation module in the present embodiment further includes Kalman filtering module and Morphological scale-space module.
Further include abnormal parameters processing module in adjusting parameter generation module in the present embodiment, for surpassing when adjusting parameter
When crossing predetermined threshold, command signal is sent out to video acquisition module, video acquisition module is by max-thresholds adjustment video acquisition
Angle and direction is simultaneously emitted by abnormal alarm information, closes the road video transfer signal, and another road of the access comprising personal target regards
Keep pouring in defeated signal.
Safety monitoring dynamic object optimization track lock system in the present embodiment further includes image pre-processing module, is used for
It is carrying out also pre-processing each frame image before target is confined using Faster RCNN, including：It is empty using HSV colors
Between alleviate influence of the target shadow to Objective extraction, salt-pepper noise is eliminated using median filter, using image binaryzation
So that image is become simple, using in iconology denoising corrosion and expansion picture noise is eliminated in extraction respectively and filling image is empty
Hole.
Video acquisition complexes in the present embodiment include photographic device and adjusting apparatus；
The photographic device includes main photographic device and second camera device, and the second camera device is two, respectively
There is 50% visual field that is superimposed with main photographic device, without superimposition between the second camera device；
The adjusting apparatus is used to adjust the angle of the video acquisition of photographic device according to the adjusting parameter that communication module transmits
Degree and direction.
The size of the number K values of the Gauss model of fitting in this implementation is 4.
Significant progress and substantive distinguishing features in the present embodiment compared with the existing technology are analyzed as follows：
First video image is pre-processed in this implementation, including target shadow pair is alleviated using HSV color spaces
The influence of Objective extraction eliminates salt-pepper noise using median filter, so that image is become simple using image binaryzation, uses
Picture noise and filling image cavity are eliminated in extraction respectively for corrosion in iconology denoising and expansion, confined for subsequent target and
Foreground extraction reduces the bit error rate and difficulty, special to select these types operation that reach synergistic effect, at video image
The factor that may be influenced in reason is substantially investigated one time, and the precision of subsequent processing is effectively raised；
Secondly creative in the application that first personal target is substantially confined using Faster RCNN, then using mixed
Close Gauss model carry out foreground extraction can effectively solve due to camera shake etc. caused by background variation acutely so as to cause
The bad problem of foreground extraction effect, after substantially being confined to personal target using Faster RCNN, frame is just used as one
The foundation that a noise is eliminated carries out foreground extraction using mixed Gauss model when subsequently carrying out foreground extraction, and uses picture
Vegetarian refreshments models, and to newly being compared one by one into pixel, while each frame new images in this way can into mixed Gauss model is all updated
The degree of fitting of Gauss model is increased as possible, by test of many times in the present embodiment, the Gauss model being fitted using 4 can be
It is achieved a better balance in speed and effect；
The application, using coordinate system is established, determines the position of person target, obtains its variable quantity after the determination of personal target,
So as to adjust the angle and direction of video acquisition or even the transmission path of Switch Video, quickly will clearly be needed to accurate
The target video image acquisition storage transmission of tracking deletes picture frame width number additionally by comparing displacement distance to be spaced,
The data volume for effectively reducing transmission has saved resource and has improved rate, also ensures data accurate and effective；
Kalman filtering and Morphological scale-space also have been carried out to it before personal target final output in the present embodiment, it can
Realize the smooth of personal target so that track to obtain better vision and the effect of sense organ in humanbody moving object.
In the overall process that the application tracks the extraction of personal target, whole optimization and design so that Objective extraction is more
For accurate, tracking is more timely, feedback is more smoothly, practical effect impression is more preferable, have compared with the existing technology notable
Progress and substantive distinguishing features.
Flow chart and block diagram in attached drawing show the system, method and computer journey of multiple embodiments according to the present invention
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part for a part for one module, section or code of table, the module, section or code includes one or more uses
The executable instruction of the logic function as defined in realization.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two continuous boxes can essentially base
Originally it is performed in parallel, they can also be executed in the opposite order sometimes, this is depended on the functions involved.It is also noted that
It is the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart, can uses and execute rule
The dedicated hardware based system of fixed function or action is realized, or can use the group of specialized hardware and computer instruction
It closes to realize.
A kind of safety monitoring dynamic object optimization track lock method of the present invention can be extracted accurately in video image
Personal target, and collected according to the acquisition angles and direction, moment complete display of the mobile synchronous adjustment video of target
The image of personal target had both reduced the accurate and effective that data volume in transmission of video in turn ensures personal target information, had had
Higher practical value and it is widely applied foreground.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Profit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent requirements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiment being appreciated that.
Claims (10)
1. a kind of safety monitoring dynamic object optimizes track lock method, which is characterized in that include the following steps：
S1, Security Personnel, which authorize, to be logged in, wake-up safety monitoring service, personal infrared signal in first cycle detection ambient enviroment,
Following operation is taken after detecting personal infrared signal：
S11, detection video camera apparatus, if detecting video camera apparatus, automatic start is transferred to step S12, if being not detected
Video camera apparatus then sends a warning message and Security Personnel is reminded correctly to install, until being successfully detected；
S12, video data is acquired in real time, video data is separated into image data and voice data to be stored sequentially in time；
S13, it takes image data pretreatment measure mainly and includes：Target shadow is alleviated to target using HSV color spaces
The influence of extraction eliminates salt-pepper noise using median filter, so that image is become simple using image binaryzation, using image
Learning corrosion and expansion in denoising, picture noise and filling image cavity are eliminated in extraction respectively；
S2, each frame image data of the video data of step S1 acquisitions is confined using Faster RCNN progress target, is generated
It is pending according to prefixed time interval T stage extractions storage etc. will to generate the image data in Suggestion box for Suggestion box；
Image data mixed Gauss model in S3, the generation Suggestion box for storing stage extraction in step S2 carries out foreground extraction
Identification obtains personal target data, including：
S31, any pixel point in the preceding M frames image in this section of image data is modeled to obtain the probability distribution of the pixel
Function：
X indicates the pixel gray value, and at the time of t is that the frame image corresponds to, K is the number for the Gauss model for choosing fitting, and η is
Gaussian probability-density function, ω belong to the weight of different functions, and μ and ε are the mean vector of n-th of Gauss model of t moment respectively
And covariance matrix；
S32, probability-distribution function P (X are being obtainedt) after, since M+1 frames, updates and mix after being obtained to each frame image data
Gauss model is matched with each pixel in present image with mixed Gauss model, if matching, then it is assumed that the pixel is
Background pixel；Otherwise foreground pixel data are extracted as；
S33, the personal target data in each frame image is obtained according to the foreground pixel data combined treatment extracted in S32；
S4, rectangular coordinate system is established as origin using the central point of every frame image, obtains the location parameter of personal target in step S3,
The running orbit of personal target is obtained according to the difference of location parameter between consecutive frame image；
S5, according to the running orbit of personal target, obtain including adjustment time interval, angle information, elevation information, movement velocity
Adjusting parameter including information adjusts the angle and direction of video acquisition to ensure personal target according to obtained adjusting parameter
Steadily it is restored at image center position；
S6, the video data of acquisition is transmitted to after handling squeeze operation to server storage user is waited for retrieve for examination, the place
Reason squeeze operation includes the following steps：
S61, the personal target handled according to step 4 location parameter according to preset frame number Q, calculate adjacent Q frames figure
Personal target location variation difference △ S as between；
S62, when target location variation difference △ S be more than predetermined threshold value Y when, by adjacent Q frames image whole compression transmission to service
Device；
When variation difference △ S are less than predetermined threshold value Y when target location, adjacent Q frames image is removed into a frame according to every a frame
Image, the video image compression for obtaining scaled-down version are sent into server storage.
2. a kind of safety monitoring dynamic object according to claim 1 optimizes track lock method, which is characterized in that described
Include Kalman filtering and Morphological scale-space to the processing of foreground pixel data in step S33.
3. a kind of safety monitoring dynamic object according to claim 1 optimizes track lock method, which is characterized in that described
Step S5 is further comprising the steps of：
If adjusting parameter is more than predetermined threshold, the angle and direction of video acquisition is adjusted by max-thresholds, is simultaneously emitted by exception
Warning message, closes the road video transfer signal, and access includes the another road video transfer signal of personal target.
4. a kind of safety monitoring dynamic object according to claim 1 optimizes track lock method, which is characterized in that described
Distance in S4 representated by the per unit of rectangular coordinate system is according to image minimum resolution institute's generation in advance calibrated visual field
The minimum range of table converts to obtain.
5. a kind of safety monitoring dynamic object optimizes track lock system, which is characterized in that including：
Login module authorizes for providing user's logentry and wakes up safety monitoring dynamic object optimization track lock system；
Video acquisition module, including multi-channel video capturing complexes detach video data for acquiring video data in real time
Stored sequentially in time etc. at image data and voice data it is pending, and according to adjustment signal adjustment per road video acquisition
Angle and direction；
Personal detection module is touched for personal infrared signal in cycle detection ambient enviroment after detecting personal infrared signal
Send out video acquisition module；
Memory module acquires video data in real time for storing video acquisition；
Personal target confines processing module, the video data for extracting memory module storage, and each frame image data is made
Target is carried out with Faster RCNN to confine, generates Suggestion box, will generate the image data in Suggestion box according to prefixed time interval
T stage extractions storage etc. is pending；
Foreground extracting module carries out foreground for the image data mixed Gauss model in the generation Suggestion box of stage extraction storage
Extraction identification obtains personal target data, including：
Data modeling module, for being modeled to obtain the picture to any pixel point in the preceding M frames image in this section of image data
The probability-distribution function of vegetarian refreshments：
X indicates the pixel gray value, and at the time of t is that the frame image corresponds to, K is the number for the Gauss model for choosing fitting, and η is
Gaussian probability-density function, ω belong to the weight of different functions, and μ and ε are the mean vector of n-th of Gauss model of t moment respectively
And covariance matrix；
Pixel classifications module, for obtaining probability-distribution function P (Xt) after, since M+1 frames, to each frame image data
Mixed Gauss model is updated after acquisition, is matched with mixed Gauss model with each pixel in present image, if matching,
Think that the pixel is background pixel；Otherwise foreground pixel data are extracted as；
Personal target generation module, for obtaining the person in each frame image according to the foreground pixel data combined treatment of extraction
Target data；
Location position module obtains the position of personal target for establishing rectangular coordinate system as origin using the central point of every frame image
Parameter is set, the running orbit of personal target is obtained according to the difference of location parameter between consecutive frame image；
Adjusting parameter generation module obtains including adjustment time interval, angle letter for the running orbit according to personal target
Adjusting parameter including breath, elevation information, motion velocity information, and adjusting parameter is fed back into video acquisition module, video is adopted
Collection module adjusts the angle and direction of video acquisition to ensure that personal target is steadily restored in place according to obtained adjusting parameter
At image center position；
Video data compression processing module, for, according to preset frame number Q, being calculated according to the location parameter of obtained personal target
Go out the variation difference △ S of the personal target location between adjacent Q frames image；When target location, variation difference △ S are more than predetermined threshold value Y
When, by adjacent Q frames image whole compression transmission to server；When target location, variation difference △ S are less than predetermined threshold value Y,
Adjacent Q frames image is removed into a frame image according to every a frame, obtains the vedio data of scaled-down version and compression；
Communication module, for the video data transmission that acquires video acquisition module to memory module, video data pressure will be passed through
Contracting processing module treated vedio data is sent into server storage and adjusting parameter is transmitted to video acquisition module.
6. a kind of safety monitoring dynamic object according to claim 5 optimizes track lock system, which is characterized in that described
Personal target generation module further includes Kalman filtering module and Morphological scale-space module.
7. a kind of safety monitoring dynamic object according to claim 5 optimizes track lock system, which is characterized in that described
Further include abnormal parameters processing module in adjusting parameter generation module, is used for when adjusting parameter is more than predetermined threshold, to video
Acquisition module sends out command signal, and video acquisition module is simultaneously emitted by by the angle and direction of max-thresholds adjustment video acquisition
Abnormal alarm information, closes the road video transfer signal, and access includes the another road video transfer signal of personal target.
8. a kind of safety monitoring dynamic object according to claim 5 optimizes track lock system, which is characterized in that also wrap
Image pre-processing module is included, for carrying out also having carried out pre- place to each frame image before target is confined using Faster RCNN
Reason, including：Influence of the target shadow to Objective extraction is alleviated using HSV color spaces, green pepper is eliminated using median filter
Salt noise, using image binaryzation make image become it is simple, using in iconology denoising corrosion and expansion respectively extraction eliminate
Picture noise and filling image cavity.
9. a kind of safety monitoring dynamic object according to claim 5 optimizes track lock system, which is characterized in that described
Video acquisition complexes include photographic device and adjusting apparatus；
The photographic device includes main photographic device and second camera device, and the second camera device is two, respectively with master
Photographic device has a 50% superposition visual field, without superimposition between the second camera device；
The adjusting parameter that the adjusting apparatus is used to transmit according to communication module adjust the angle of the video acquisition of photographic device with
Direction.
10. a kind of safety monitoring dynamic object according to claim 5 optimizes track lock system, which is characterized in that institute
The size for stating K values is 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810575819.9A CN108805073A (en) | 2018-06-06 | 2018-06-06 | A kind of safety monitoring dynamic object optimization track lock method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810575819.9A CN108805073A (en) | 2018-06-06 | 2018-06-06 | A kind of safety monitoring dynamic object optimization track lock method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108805073A true CN108805073A (en) | 2018-11-13 |
Family
ID=64087235
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810575819.9A Withdrawn CN108805073A (en) | 2018-06-06 | 2018-06-06 | A kind of safety monitoring dynamic object optimization track lock method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108805073A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110246174A (en) * | 2019-07-08 | 2019-09-17 | 北京派克盛宏电子科技有限公司 | A kind of farm live pig method of counting |
| CN110427865A (en) * | 2019-07-29 | 2019-11-08 | 三峡大学 | High voltage prohibited area human behavior video features picture extracts and reconstructing method |
| CN112383754A (en) * | 2020-11-12 | 2021-02-19 | 珠海大横琴科技发展有限公司 | Monitoring method and device for early warning object, electronic equipment and storage medium |
| CN112991742A (en) * | 2021-04-21 | 2021-06-18 | 四川见山科技有限责任公司 | Visual simulation method and system for real-time traffic data |
-
2018
- 2018-06-06 CN CN201810575819.9A patent/CN108805073A/en not_active Withdrawn
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110246174A (en) * | 2019-07-08 | 2019-09-17 | 北京派克盛宏电子科技有限公司 | A kind of farm live pig method of counting |
| CN110427865A (en) * | 2019-07-29 | 2019-11-08 | 三峡大学 | High voltage prohibited area human behavior video features picture extracts and reconstructing method |
| CN110427865B (en) * | 2019-07-29 | 2023-08-25 | 三峡大学 | Human behavior video feature picture extraction and reconstruction method for high-voltage forbidden region |
| CN112383754A (en) * | 2020-11-12 | 2021-02-19 | 珠海大横琴科技发展有限公司 | Monitoring method and device for early warning object, electronic equipment and storage medium |
| CN112991742A (en) * | 2021-04-21 | 2021-06-18 | 四川见山科技有限责任公司 | Visual simulation method and system for real-time traffic data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108769595A (en) | A kind of intelligence is personal to identify monitoring tracking transmission method and system | |
| CN108806146A (en) | A kind of safety monitoring dynamic object track lock method and system | |
| CN108765354A (en) | A kind of data-optimized transmission method of dynamic object recognition tracking image and system | |
| CN108805073A (en) | A kind of safety monitoring dynamic object optimization track lock method and system | |
| CN108711162A (en) | A kind of intelligence is personal to identify monitoring optimization tracking transmission method and system | |
| CN105518709B (en) | The method, system and computer program product of face for identification | |
| EP2864930B1 (en) | Self learning face recognition using depth based tracking for database generation and update | |
| CN104751108B (en) | Facial image identification device and facial image recognition method | |
| CN108596041B (en) | A kind of human face in-vivo detection method based on video | |
| CN108805837A (en) | A kind of dynamic object efficiently tracks image data transfer method and system | |
| CN103971386B (en) | A kind of foreground detection method under dynamic background scene | |
| CN104050449B (en) | A kind of face identification method and device | |
| CN105243664B (en) | A kind of wheeled mobile robot fast-moving target tracking method of view-based access control model | |
| CN108876672A (en) | A kind of long-distance education teacher automatic identification image optimization tracking and system | |
| CN107657244B (en) | Human body falling behavior detection system based on multiple cameras and detection method thereof | |
| CN109145803A (en) | Gesture identification method and device, electronic equipment, computer readable storage medium | |
| CN105426827A (en) | Living body verification method, device and system | |
| CN110084165A (en) | The intelligent recognition and method for early warning of anomalous event under the open scene of power domain based on edge calculations | |
| CN108810464A (en) | A kind of intelligence is personal to identify image optimization tracking transmission method and system | |
| CN110110646A (en) | A kind of images of gestures extraction method of key frame based on deep learning | |
| CN104091173B (en) | A kind of gender identification method and device based on web camera | |
| CN108833776A (en) | A kind of long-distance education teacher automatic identification optimization tracking and system | |
| CN110175553B (en) | Method and device for establishing feature library based on gait recognition and face recognition | |
| CN106447695A (en) | Method and device for judging same object in multi-object tracking | |
| CN104899559B (en) | A kind of rapid pedestrian detection method based on video monitoring |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20181113 |
|
| WW01 | Invention patent application withdrawn after publication |