CN106096499A - A kind of video image culminant star moon pattern detection method and system - Google Patents

A kind of video image culminant star moon pattern detection method and system Download PDF

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CN106096499A
CN106096499A CN201610363846.0A CN201610363846A CN106096499A CN 106096499 A CN106096499 A CN 106096499A CN 201610363846 A CN201610363846 A CN 201610363846A CN 106096499 A CN106096499 A CN 106096499A
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star moon
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张德馨
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TIANJIN ISECURE TECHNOLOGY Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames

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Abstract

The present invention relates to the method and system of a kind of video image culminant star moon pattern detection, the method is by a frame video image area-of-interest；Area-of-interest is carried out foreground detection by default foreground detection method and obtains foreground image；With the detection of classifier star moon pattern comprising default star moon feature in foreground image profile, this foreground image of labelling also outwards expands the statistical regions that one default enlarged area of formation detects as subsequent frame image centered by this foreground image region, then with the star moon pattern in statistical regions in this foreground image place frame of detection of classifier later continuous print predetermined number two field picture, alarm is exported when the number of times detected is eligible；Thus provide a kind of effective detection means for effectively carrying out star moon pattern detection in video image.

Description

A kind of video image culminant star moon pattern detection method and system
Technical field
The invention belongs to image identification technical field, be specifically related to the method for a kind of video image star moon pattern detection and be System.
Background technology
In some unsafe areas, for the needs of social safety, need some personages wearing special dress ornament are entered Row prevention detection the most in advance identifies.Although the image that can use image capture device Real-time Collection monitoring region at present enters Row monitoring, but existing monitoring technology is only monitoring afterwards, for some non-persistent personages such as the band star moon pattern mark people Thing cannot be accomplished detect identification in advance and give warning in advance, it is impossible to accomplishes to monitor in advance, prevent in advance.
Summary of the invention
It is an object of the invention to solve above-mentioned technical problem and provide a kind of video image culminant star moon pattern detection Method and system.
For achieving the above object, the present invention adopts the following technical scheme that
A kind of video image culminant star moon pattern detection method, comprise the following steps:
Obtain a frame video image and area-of-interest to be detected in this frame video image is set；
Described area-of-interest is carried out foreground detection, the foreground image of output binaryzation by default foreground detection method；
Obtain described foreground image profile and in this profile with the detection of classifier comprising default star moon feature whether There is star moon pattern；
If star moon pattern detected in this profile, then this foreground image of labelling be with this foreground image region The heart outwards expands and forms the statistical regions that a default enlarged area detects as subsequent frame image, then examines with described grader Survey in this foreground image place frame continuous print predetermined number two field picture later in this statistical regions, whether have star moon pattern；
If detecting in described predetermined number two field picture, the frame number of star moon pattern more than presetting flase drop frame number, then exports report Alert prompting.
Described area-of-interest is the human face region that in this frame video image, definition meets default definition values.
Described default foreground detection method is Vibe foreground detection method.
Described grader is adaboost cascade of strong classifiers.
The step tool of the profile of the described foreground image of described acquisition is:
After the foreground image of described binaryzation does repeatedly corrosion, connection becomes a region, then is obtained by rim detection The profile of foreground image place rectangle.
The present invention also aims to provide the system of a kind of video image culminant star moon pattern detection, including:
Detection region arranges module, is used for obtaining a frame video image and arranges sense to be detected in this frame video image emerging Interest region；
Foreground image acquisition module, for described area-of-interest is carried out foreground detection by default foreground detection method, The foreground image of output binaryzation；
Pattern detection module, for obtaining the profile of described foreground image and with comprising default star moon feature in this profile Detection of classifier whether have star moon pattern；
Subsequent frame detection module, after star moon pattern detected in this profile, this foreground image of labelling with before this Outwards expand centered by scape image region and form the statistical regions that a default enlarged area detects as subsequent frame image, Then with in continuous print predetermined number two field picture after this foreground image place frame of described detection of classifier in this statistical regions Whether there is star moon pattern；
Alarm module, for detecting that in described predetermined number two field picture the frame number of star moon pattern is more than presetting flase drop Frame number, exports alarm.
The present invention, by above technical scheme, can detect on the character physical in image rapidly in video image Whether there is star moon pattern, and report to the police when the number of times detecting star moon pattern reaches the secondary numerical value set, thus for regard Frequently image detects on the character physical in image the star moon pattern effectiveness provide one and effectively detect solution.
Accompanying drawing explanation
Fig. 1 provides for the embodiment of the present invention the video image culminant star moon pattern detection the flow chart of method；
The schematic diagram of cascade of strong classifiers shown in Fig. 2.
Detailed description of the invention
Below, in conjunction with example, substantive distinguishing features and the advantage of the present invention are further described, but the present invention not office It is limited to listed embodiment.
Shown in Figure 1, a kind of video image culminant star moon pattern detection method, including:
S101: obtain a frame video image and area-of-interest to be detected in this frame video image is set；
S102: described area-of-interest is carried out foreground detection, the prospect of output binaryzation by default foreground detection method Image；
S103: obtain the profile of described foreground image and with the detection of classifier comprising default star moon feature in this profile Whether there is star moon pattern；
S104: if star moon pattern being detected in this profile, then this foreground image of labelling with this foreground image location Outwards expand centered by territory and form the statistical regions that a default enlarged area detects as subsequent frame image, then with described point Class device detects in this foreground image place frame continuous print predetermined number two field picture later whether have star moon figure in this statistical regions Case；
S105: the frame number of star moon pattern is more than presetting flase drop frame number, then if detecting in described predetermined number two field picture Output alarm, otherwise returns S101 detection.
Wherein, in the present invention, described area-of-interest is that in this frame video image, definition meets default definition values Human face region, i.e. refers to human face region region the most clearly in whole video image.
Concrete, in step S101, obtain a frame video image and arrange in this frame video image to be detected interested Region particularly as follows:
Obtain the width of sequence of video images and height, according to the width of sequence of video images and high calculate area-of-interest Position and width in video image are high, thus realize the setting to area-of-interest.
After arranging area-of-interest, follow-up detection will be carried out in area-of-interest, by arranging region of interest Territory, fewer than detection time in entire image a lot, thus improve detection speed.
In described step S102, described area-of-interest is carried out foreground detection by default foreground detection method, mainly In order to judge whether described area-of-interest exists foreground image, as there is the image then exporting binaryzation, otherwise return S101 detects, and when foreground image having been detected, obtains the profile of foreground image and with comprising star moon feature in prospect profile Grader detect, to determine whether star moon pattern.
The described grader comprising star moon feature is adaboost cascade classifier.
In the present invention, described default foreground detection method is Vibe (Visual Background extractor) prospect Detection method.
Wherein, in the present invention, the step tool of the profile of the described foreground image of described acquisition is:
After the foreground image of described binaryzation does repeatedly corrosion, connection becomes a region, then is obtained by rim detection The profile of foreground image place rectangle.
Described Vibe foreground detection method i.e. utilizes ViBe model to realize, below to ViBe foreground detection model Illustrate.
1, ViBe model operation principle
Background object refers to the static or object slowly moved, and foreground object is the object of corresponding movement. Object detection can be regarded as a classification problem, namely determine whether a pixel belongs to background dot.At ViBe mould In type, background model is that each background dot stores a sample set, then each new pixel value and sample set is carried out Relatively judge whether to belong to background dot, thus may know that if a new observed value belongs to background dot, then it should With the sampled value in sample set relatively.
Concretely, note v (x): the pixel value at x point；M (x)=V1, V2 ... VN} is the background sample set (sample at x This collection size is N)；SR (v (x)): R centered by the x region as radius, if M (x) [SR (v (x)) ∩ v1, v2 ..., VN}}] more than given threshold value min, it is considered that x point belongs to background dot.
2, ViBe model initialization method
ViBe initializes the process of the sample set being exactly filler pixels, owing to can not comprise pixel in a two field picture Spatial and temporal distributions information, may utilize close pixel and had close spatial and temporal distributions characteristic, specifically: for a picture Vegetarian refreshments, the pixel value of the random neighbours' point selecting it is as its model sample value.M0 (x)=v0 (y | y ∈ NG (x)) }, t =0 initial time, NG (x) is neighbours' point.This initial method advantage is that the reaction for noise is sensitiveer, amount of calculation Little speed is fast, can the detection carrying out moving object quickly.
3, ViBe model modification strategy
More New Policy can be conservative more New Policy+foreground point method of counting.Foreground point counts: unite pixel Meter, if certain pixel N continuous time is detected as prospect, is then updated to background dot.Randomly sub-sampled: new at each Frame of video in all go to update the sample value of each pixel in background model it is not necessary that, when a pixel quilt When being categorized as background dot, it has the probability of 1/ φ to go to update background model.
Concrete update method: each background dot has the probability of 1/ φ to go to update the model sample value of oneself, also has simultaneously The probability of 1/ φ goes to update the model sample value of its neighbours' point.The sample value of more new neighbor make use of the spatial transmission of pixel value Characteristic, background model is gradually to external diffusion, and this is also beneficial to the faster identification in Ghost region.Reach when foreground point counting simultaneously It is changed into background during to marginal value, and has the probability of 1/ φ to go to update the model sample value of oneself.Selecting sample to be replaced When the sample value of this concentration, randomly select a sample value and be updated, so can ensure that the smooth life of sample value Cycle, the probability that such a sample value is not updated at moment t was (N-1)/N owing to being random renewal, it is assumed that the time is Continuous print, then after past time of dt, the probability that sample value still retains is:
P ( t , t + d t ) = ( N - 1 N ) ( t + d t ) - t
Also can write,
P ( t , t + d t ) = e - ln ( N N - 1 ) d t .
Showing the most whether a sample value is replaced unrelated with time t, randomized policy is suitable.
Drawn by foreground detection is a bianry image, and foreground image pixel value is 1, and background image pixel values is 0, It is not a connected region due to foreground image, this bianry image is done 5 corrosion, connected and become a region, then The size and location of foreground image areas place rectangle is obtained by rim detection, so that it is determined that the profile of foreground image.
Wherein, in the present invention, described grader is adaboost cascade classifier.Adaboost cascade classifier is to pass through Acquire the positive sample of 5000 features comprising star moon pattern and an adaboost cascade point that 300,000 negative samples are trained to Class device.Detecting at the contour area of foreground image with this grader, if detecting star moon pattern, exporting it in the picture Position and size.
The principle of Adaboost cascade classifier is as follows:
Adaboost cascade classifier is to utilize Weak Classifier that classification capacity is general by certain method superposition (boost) get up, the strong classifier that composition and classification is very capable.The arthmetic statement of Adaboost training strong classifier is as follows:
Given a series of training sample (x1,y1),(x2,y2),...(xn,yn), wherein xiRepresent i-th sample, yiWhen=1 For positive sample (face), yi=0 represents negative sample (non-face).To each feature featurej, train a Weak Classifier hj (x), the Weak Classifier afterwards each feature generated calculating weighted error:
ϵ j = Σ i ω i | h j ( x i ) - y i | .
The grader with minimal weight error e j is added in strong classifier, and the probability updating training sample divides Cloth:
D j ( i ) = D j ( i ) β j 1 - e i
Wherein βj=εj/1-εj, ei=0 represents that sample xi is correctly classified, and ei=1 represents and correctly do not classified, final structure One-tenth strong classifier:
H ( x ) = s i g n [ Σ j = 1 h j ( x ) - b ]
Wherein, b is the threshold value arranged, and is defaulted as 0.
Adaboost cascade classifier as shown in Figure 2, by multiple Haar-like features F1F2…FnJointly form certainly Plan tree, by inputting data and the plurality of Haar-like feature F1F2…FnCompare, it is judged that meet and be output as face (face), the most incongruent it is output as non-face (not face).
Viola-Jones detector utilizes waterfall (Cascade) algorithm classification device to be organized as the cascade classifier of screening type, Each node of cascade is that AdaBoost trains the strong classifier obtained.Each node in cascade arranges threshold value b so that almost All face samples can be transferred through, and most non-face sample can not pass through.Node arranges from simple to complex, and position is more Node rearward is the most complicated, i.e. comprises the most Weak Classifiers.So energy minimization refusal image but amount of calculation during region, logical Know and ensure the high detection rate of grader and low reject rate.Such as it is 99.9% at discrimination, when reject rate is 50%, (99.9% Face and 50% non-face can pass through), total discrimination of 20 nodes is: 98%, and false acceptance rate is only: 0.0001%.
In the present invention, in order to ensure the precision of detection, the method for successive frame statistics is used to get rid of flase drop, concrete, permissible It is to add up in units of 10 frames, when certain frame detects star moon pattern, according to previously obtained foreground image areas at image In position and size, centered by the rectangle of foreground image areas place, outwards expand the 1/3 of its length of side, using this region as Statistical regions, if the star moon pattern detected in this statistical regions in continuous print 9 frame after this frame is less than 5 frames, it is determined that for by mistake Inspection, returns step S101 and continues detection, be otherwise judged to just detecting, and output is reported to the police.
The present invention also aims to provide the system of a kind of video image culminant star moon pattern detection, including:
Detection region arranges module, is used for obtaining a frame video image and arranges sense to be detected in this frame video image emerging Interest region；
Foreground image acquisition module, for described area-of-interest is carried out foreground detection by default foreground detection method, The foreground image of output binaryzation；
Pattern detection module, for obtaining the profile of described foreground image and with comprising default star moon feature in this profile Detection of classifier whether have star moon pattern；
Subsequent frame detection module, after star moon pattern detected in this profile, this foreground image of labelling with before this Outwards expand centered by scape image region and form the statistical regions that a default enlarged area detects as subsequent frame image, Then with in continuous print predetermined number two field picture after this foreground image place frame of described detection of classifier in this statistical regions Whether there is star moon pattern；
Alarm module, for detecting that in described predetermined number two field picture the frame number of star moon pattern is more than presetting flase drop Frame number, exports alarm.
The present invention, by above technical scheme, can detect on the character physical in image rapidly in video image Whether there is star moon pattern, and report to the police when the number of times detecting star moon pattern reaches the secondary numerical value set, thus for regard Frequently image detects on the character physical in image the star moon pattern effectiveness provide one and effectively detect solution.
This video image culminant star moon pattern detection the detection process of system and method, refer to foregoing video The image culminant star moon pattern detection the detection process of method and method, be the most no longer described in detail.
The present invention, by above technical scheme, can detect on the character physical in image rapidly in video image Whether there is star moon pattern, and report to the police when the number of times detecting star moon pattern reaches the secondary numerical value set, thus for regard Frequently image detects on the character physical in image the star moon pattern effectiveness provide one and effectively detect solution.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For Yuan, under the premise without departing from the principles of the invention, it is also possible to make some improvements and modifications, these improvements and modifications also should It is considered as protection scope of the present invention.

Claims (10)

1. one kind the video image culminant star moon pattern detection method, it is characterised in that comprise the following steps:
Obtain a frame video image and area-of-interest to be detected in this frame video image is set；
Described area-of-interest is carried out foreground detection, the foreground image of output binaryzation by default foreground detection method；
Obtain the profile of described foreground image and whether have star with the detection of classifier comprising default star moon feature in this profile Month pattern；
If star moon pattern being detected in this profile, then this foreground image of labelling and centered by this foreground image region to Outer expansion forms the statistical regions that a default enlarged area detects as subsequent frame image, then should with described detection of classifier After the frame of foreground image place, whether continuous print predetermined number two field picture there is star moon pattern in this statistical regions；
If detecting in described predetermined number two field picture, the frame number of star moon pattern more than presetting flase drop frame number, then exports warning and carries Show.
The most according to claim 1 the video image culminant star moon pattern detection method, it is characterised in that described area-of-interest The human face region of default definition values is met for definition in this frame video image.
The most according to claim 1 the video image culminant star moon pattern detection method, it is characterised in that described default prospect examine Survey method is Vibe foreground detection method.
The most according to claim 1 the video image culminant star moon pattern detection method, it is characterised in that described grader is Adaboost cascade of strong classifiers.
5. according to described in any one of claim 1-4 the video image culminant star moon pattern detection method, it is characterised in that described in obtain The step tool of the profile taking described foreground image is:
After the foreground image of described binaryzation does repeatedly corrosion, connection becomes a region, then obtains prospect by rim detection The profile of image place rectangle.
6. one kind the video image culminant star moon pattern detection system, it is characterised in that including:
Detection region arranges module, for obtaining a frame video image and arranging region of interest to be detected in this frame video image Territory；
Foreground image acquisition module, for described area-of-interest is carried out foreground detection by default foreground detection method, output The foreground image of binaryzation；
Pattern detection module, for obtaining the profile of described foreground image and with comprising dividing of default star moon feature in this profile Class device has detected whether star moon pattern；
Subsequent frame detection module, after star moon pattern detected in this profile, this foreground image of labelling with this foreground picture The statistical regions that a default enlarged area detects is formed as subsequent frame image, then as outwards expanding centered by region With in continuous print predetermined number two field picture after this foreground image place frame of described detection of classifier in this statistical regions whether There is star moon pattern；
Alarm module, for detecting that in described predetermined number two field picture the frame number of star moon pattern is more than presetting flase drop frame Number, exports alarm.
The most according to claim 6 the video image culminant star moon pattern detection system, it is characterised in that described area-of-interest The human face region of default definition values is met for definition in this frame video image.
The most according to claim 6 the video image culminant star moon pattern detection system, it is characterised in that described default prospect examine Survey method is Vibe foreground detection method.
The most according to claim 6 the video image culminant star moon pattern detection system, it is characterised in that described grader is Adaboost cascade of strong classifiers.
10. according to described in any one of claim 6-9 the video image culminant star moon pattern detection system, it is characterised in that described Pattern detection module includes:
Image outline acquiring unit, after the foreground image of described binaryzation is done repeatedly corrosion, connection becomes a region, The profile of foreground image is obtained again by rim detection.
CN201610363846.0A 2016-05-26 2016-05-26 A kind of video image culminant star moon pattern detection method and system Pending CN106096499A (en)

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