CN108062764A - A kind of object tracking methods of view-based access control model - Google Patents

A kind of object tracking methods of view-based access control model Download PDF

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CN108062764A
CN108062764A CN201711243635.4A CN201711243635A CN108062764A CN 108062764 A CN108062764 A CN 108062764A CN 201711243635 A CN201711243635 A CN 201711243635A CN 108062764 A CN108062764 A CN 108062764A
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object tracking
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贾文峰
赵小星
李博闻
雷旭
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Jiyi Robot (shanghai) Co Ltd
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Abstract

The present invention is a kind of long-time object tracking method of view-based access control model, and step includes：Input is used as each two field picture of mobile phone acquisition, target to be tracked is selected by subscriber frame, runs multiple dimensioned KCF trackers.Tracking creditability is calculated by peak sidelobe ratio during tracking, tracking filter model is updated in high confidence level.When confidence level is less than certain threshold value, it was demonstrated that tracking object is blocked, and runs YOLO detection process at this time.YOLO detects multiple alternative targets, makees essence detection, the maximum as tracking target of the value that meets with a response according to the tracking filter model and alternative target that have learnt at this time.This method tracking target is accurate, and real-time is good.

Description

A kind of object tracking methods of view-based access control model
Technical field
The present invention relates to object tracking methods more particularly to a kind of object tracking methods of view-based access control model.
Background technology
Tracking moving object is one of research emphasis problem of computer vision field, is all had in many occasions important Using, with reference to three-axis stabilization holder realize tracing control function be one of its application scenario.The speed of service is fast at present and effect compared with Good tracking is that nuclear phase closes algorithm filter KCF and is with the addition of the DSST algorithms of dimension self-adaption function.KCF is used The circular matrix of target peripheral region gathers positive negative sample, using ridge regression training objective detector, using circular matrix in Fu In vane space can the property of diagonal angling the computing of matrix is converted into point multiplication operation, greatly reduce operand, improve fortune Speed is calculated, algorithm is made to meet requirement of real-time.
KCF track algorithms have a defect that model drifts about at any time, it is impossible to well adapt to as blocked, illumination variation Greatly, situations such as background interference is larger can only be used, it is necessary to reference to detection module as short time tracking filter, lost in tracking The rectangle frame of tracking module can be reset when effect.Detection process is generally time-consuming longer, because to be search for whole pictures Processing reason.The method generally used is tracking-detection scheme as TLD algorithms, and individually one grader of training is used to examine It surveys, the confidence level during correlation filter tracking, such as maximum response, peak sidelobe ratio etc. is calculated in real time, in high confidence level Shi Gengxin tracking results and correlation filter model, while update the positive negative sample of detection module, during low confidence, operation detection Module continues to track after detecting target.
The shortcomings that above-mentioned way is：1st, together with the update of model modification process and tracking result is, it is impossible to ensure well Model drift can be reduced and keep up with target as far as possible；It, individually one detection point of training simultaneously when the 2nd, tracking process operation Class device adds more computation burden, declines algorithm real-time；3rd, detection process use sliding window method, if inefficiency and Trace model has drifted about, and is readily detected false target and tracks.
YOLO (You only look once) detection method is a kind of object detector end to end, passes through point in space The probability of bounding box and respective classes is cut into, using individual network directly from the general of whole image predicted boundary frame and classification Rate.By the training pattern on Pascal VOC (a kind of to assess image classification, detection, the data set split), 20 are can detect that Remaining kind of common object, such as people, animal, the vehicles, indoor object.
Better familiar object tracking can be realized with reference to YOLO detectors and KCF tracking filters.With prior art phase Than, it is an advantage of the invention that：High confidence level updates tracking filter and greatly reduces filter drift；It is not required to during tracking Individual grader is trained to improve real-time；Operation deep learning neutral net does object detection, obtains more accurately examining It surveys as a result, being influenced smaller by trace model drift.
The content of the invention
In view of the above problems, for overcome the deficiencies in the prior art, the present invention provides a kind of tracking better base of accuracy In the object tracking methods of vision.
A kind of object tracking methods of view-based access control model, include the following steps：
S1：Subscriber frame runs tracking process after selecting target to be tracked, calculates the peak sidelobe ratio during tracking in real time PSR when PSR is more than threshold tau 1, starts KCF tracking and KCF detection process, updates tracking result and correlation filter model, When PSR is between τ 1 and τ 2, tracking result is only updated, does not update correlation filter model, when PSR is less than τ 2, do not performed Tracking process runs detection module；
S2：, tracking during train individual grader be used for detection module；
S3：YOLO detectors are run during detection, detect in image institute it is possible that comprising 20 kinds of common objects as into The input of one step detection；
S4：Testing result and tracking filter model are made into multiplication cross, when result is more than threshold value P, update tracking result, and Detector out of service.
Further, the KCF tracking process includes the following steps：
S11, in It frames, sampled near the pt of current location, training one recurrence device, it is described recurrence device for calculate one The response of a wicket sampling；
S12, in It+1 frames, sampled near previous frame position pt, judge the sound each sampled with the recurrence device in S11 It should；
The most strong sampling of S13, response is as this frame position pt+1.
Further, the operation YOLO detectors in the S3, specifically comprise the following steps：
When S31, tracking failure, YOLO target detection processes are activated；
Output after S32, detection is the result is that output boundary frame, the XY coordinates including bounding box rectangle, wide height, the value of the confidence, And generic etc..
S33, according to testing result, obtains several roughing results；
S34, roughing result is carried out selected, obtains unique maximum response, be final detection target.
Further, the object tracking methods are on Macbook Air 1.7GHz i7 processors, using Cmake+ OpenCV development platforms emulate OTB100 sequences.
Further, the object tracking methods are being based on Xcode+OpenCV development platforms, trace routine on mobile phone Real-time Transmission target frame position, from steady holder, realizes the tracing control function to target to three axis.
Further, the object tracking methods can be used for tracking a variety of different types of objects.
Further, the object tracking methods can be used for the object of 20 kinds or more of tracking.
Further, the object tracking methods are applied on mobile phone.
Further, the object tracking methods are applied on the mobile phone of hand-held holder.
In conclusion the present invention relates to a kind of object tracking methods of view-based access control model, tracked with reference to YOLO detectors and KCF Wave filter can realize better familiar object tracking.Compared with prior art, it is an advantage of the invention that：High confidence level updates Tracking filter greatly reduces filter drift；Individual grader need not be trained to improve real-time during tracking；Fortune Row deep learning neutral net does object detection, obtains more accurate testing result, is influenced smaller by trace model drift.
Description of the drawings
With reference to appended attached drawing, more fully to describe the embodiment of the present invention.However, appended attached drawing be merely to illustrate and It illustrates, and is not meant to limit the scope of the invention.
Fig. 1 is the object tracking flow chart of the prior art.
Fig. 2 is a kind of object tracking flow chart of view-based access control model of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings 1 and attached drawing 2 the present invention is further illustrated, it is but not as limiting to the invention.
KCF tracking process is as follows：
According to current frame information and before, frame information trains a correlation filter to correlation filtering, is carried out with the frame newly inputted Correlation calculations, obtained response diagram are exactly the tracking result predicted, equation below 1：
F (z)=<W, z>
Wherein w represents correlation filter model parameter, and z represents the image block where target.
KCF track the step of be：
In It frames, (adding padding) is sampled near the pt of current location, one recurrence device of training.This returns device energy Calculate the response of a wicket sampling；
It in It+1 frames, is sampled near previous frame position pt, judges the response each sampled with foregoing recurrence device；
The most strong sampling of response is as this frame position pt+1.
Using initial frame image block is selected to carry out correlation filter model training first, trained target is to minimize hits According to the distance of the true tag yi (regressive object) of the calculating label f (xi) and next frame locations of real targets of xi.It is returned using ridge Return, process equation below 2：
xm,nRepresent each image block for training pattern, i.e. original sample collection, it is added by initial tracking box Padding and Cosine Window carry out cyclic shift and obtain again.λ is regularization parameter, ym,nIt is trained regressand value f (xm,n), i.e., it is original The position of corresponding Gauss map after sample loops displacement.
Using cyclic shift characteristic, accelerate to solve in frequency domain, as shown in formula 3：
Represent the Fourier transformation of w.
Above-mentioned is that linear model solves, and using kernel function, actual nonlinear model is transformed into linear model, obtains model Shown in renewal process equation below 4：
Wherein ∧ represents Fourier transformation,For the nonlinear correlation filter model parameters in frequency domain,For training sample This auto-correlation kernel function.
Shown in the quick detection process equation below 5 of target：
Z represents candidate detection block,Represent the kernel function between all training samples and candidate detection block, f represents frequency domain Correlation filtering response namely z all cyclic shifts response, by f carry out IFFT obtain the correlation filtering of time domain Response, maximum value position represent the position where tracking target.
Tracking creditability is calculated, according to peak sidelobe ratio, equation below 6:
Wherein fmaxRepresent correlation filtering response maximum response max, definition secondary lobe is N × N image blocks, and μ and σ divide Not Biao Shi secondary lobe average value and variance.
Using τ, threshold tau 1 and τ 2 are defined.
As T ＜ τ 1, tracking failure stops tracking, is transferred to detection process；
It as 1 ＜ T ＜ τ 2 of τ, is updated without filter model, only updates tracking result；
As T ＞ τ 2, while update tracking result and tracking filter.
Detection process is as follows：
When tracking failure, YOLO target detection processes are activated.The characteristics of YOLO is maximum is that the speed of service is fast, Detection speed on iphone6s can reach 8fps, meet the requirement of real-time to detection.Pass through training on Pascal VOC Model, detection more than 20 kinds of different class of identification, such as：Automobile, bicycle, ship, animal, people etc..
Output after detection is the result is that output boundary frame, the XY coordinates including bounding box rectangle, wide height, the value of the confidence and Generic etc..
According to testing result, obtain being more than the roughing within about 10 as a result, to carry out essence to roughing result in next step Choosing, obtains unique maximum response, is final detection target.
If the classification of tracking target has been preset, such as setting tracking pedestrians, then roughing result can be further It reduces, and then improves detection efficiency.Further, if pedestrian's quantity is unique in setting tracking environmental, can skip over selected Process as long as target is reappeared in angular field of view and detected by YOLO detectors, switches to tracking process immediately.Therefore And the method is applicable to different use occasions.
Smart detection process is as follows：
For above-mentioned YOLO bounding box P, because it may be any size, it is not necessarily initial frame and selects target sizes w1 ×h1Integral multiple, so by its resize to sdw1×sdh1, wherein sdFor the padding values in KCF algorithms.
After resize pictures, trained correlation filter model before is utilizedIt is obtained using Fourier inversion Model in Time Domain α.Target detection process be correlation filter model with candidate detection block when domain operation, as shown in formula 7：
Above formula represents the response maximum of each image block of calculating, then the extreme value in maximizing.Because need not pair YOLO bounding boxes carry out cyclic shift, so need not be in frequency-domain calculations.The maximum f of f (p)max。
Detailed process is：
By image block p (YOLO bounding boxes) resize to s of candidatedw1×sdh1Size obtains new image block pr, sdw1 ×sdh1Add padding for initial tracking box.
Obtain prHOG features pzf；
Fourier transformation is made to feature, is obtained
The kernel function of time domain is calculated
The response f (p) of time domain is calculated, obtains the corresponding maximum f ' of this bounding boxmax；
Above calculating is repeated, until having traveled through all YOLO bounding boxes, obtains wherein maximum fmax。
If threshold value fδ, work as fmax>fδWhen, it was demonstrated that current detection output valve is high with correlation filter Model Matching precision, Corresponding bounding box is exported as detection, is inputted as KCF.
In general, it is as follows to track overall procedure：
1st, initialize
1) centered on frame selects target, it is target sizes to gather a size×The initial sample sz of padding sizes；
2) the Gauss value yf of regression value, i.e. sample image block is calculated, calculates Cosine Window cos_window；
3) feature is extracted to initial training sample sz, carries out cyclic shift after adding Cosine Window, that is, transform to frequency domain, obtain xf；
4) kernel function is carried out to the value xf after initial training sample shift and calculates kf, filter model is obtained according to yf and kf model_alphaf；
2nd, process is tracked
1) the candidate image block of input picture is obtained, by its resize initial sample size；
2) to the feature of candidate image block, cyclic shift is carried out after adding Cosine Window, that is, is transformed into frequency domain, obtains zf；
3) the value zf after being shifted to candidate samples carries out kernel function and calculates kzf, is obtained according to model model_alphaf and kzf Accordingly scheme to correlation filter；
4) maximum point response_max in corresponding figure is calculated, and calculates peak sidelobe ratio psr；
5) when psr is more than F2, response_max positions are updated, and obtain training image blocks, by training image Block extracts feature, and cyclic shift is carried out after adding cosine frame, obtains xf, calculates kf and obtains more new model model_alphaf；
6) when psr is more than F1 and less than F2, response_max positions are updated, but without model modification；
7) when psr is less than F1, tracking is stopped, into detection process；
3rd, detection process
1) bounding box is detected by YOLO in real time, and provides size and confidence level；
2) when confidence level is more than threshold value, smart detection is carried out, obtains candidate samples collection z_yolo；
3) Fourier inversion is carried out to correlation filter model model_alphaf, obtains model_alpha；
4) by the size resize of all candidate samples collection z_yolo elements to initial target size×Padding, as time Sampling sheet；
5) feature is extracted to candidate samples, Cosine Window is added to carry out circular matrix, that is, is transformed into frequency domain, obtains zf_yolo；
6) gaussian kernel function kf_yolo is calculated by zf_yolo and frequency-domain model model_alphaf, and kernel function is converted K_yolo is obtained to time domain；
7) correlation filtering product is made by time domain kernel function k_yolo and Model in Time Domain model_alpha, obtains time domain response Figure calculates wherein maximum, obtains detection_max；
8) candidate image blocks of the detection_max as next step tracking, continues to track.
Above-mentioned tracing detection process relies primarily on following platform to realize, in Macbook Air1.7GHz i7 processors On, using Cmake+OpenCV development platforms, OTB100 sequences are emulated, track algorithm is used based on dimensional variation KCF algorithms, feature selecting HOG, YOLO detection program are the model that Tiny-YOLO is trained based on Pascal VOC.
Above-mentioned algorithm is realized on iphone6s, based on Xcode+OpenCV development platforms, trace routine real-time Transmission Target frame position, from steady holder, realizes the tracing control function to target to three axis.
4th, simulation result
8 groups of more typical scenes of emulation testing on OTB100, it is contemplated that object rapid deformation blocks, goes out image Deng the situation for influencing long-time stable tracking success rate, the results are shown in table below：
Three kinds of algorithms are compared, colouring discrimination is as follows：
KCF Learning High confidence level updates Detection algorithm
It is red \ √ × Sliding window method
Yellow \ √ \ Sliding window method
Green \ × \ YOLO+ essence detections
For two kinds of situations that typical target is disturbed, since the peak side-lobe being calculated compares very little, and it is red The threshold value of algorithm is larger, so tracking failure；Yellow algorithm lacks drift as a result of high confidence level more new strategy making model In the case of as far as possible tracking gone up target, tracking process does not fail.The 60th frame Fig. 1), after target reappears, green Algorithm has obtained new tracking position of object by YOLO+ essence detections, and yellow algorithm detects target using sliding window method, red Color method is since the target positive sample that front is trained is less, so not detecting new target location.
For three kinds of excessive situations of typical target deformation, since deformation is excessive, so red algorithm already tracks failure , and although yellow algorithm employs high confidence level track algorithm, but is still unable to effective district partial objectives for and background, cause with Track model drifts about, and has kept up with the target of mistake, and in tracking failure, and also do not detect target again to the last afterwards. Green method is also tracked in former frames to fail, but detects target again by YOLO detectors, and increases afterwards The training sample of trace model is added so that track always afterwards successfully.Demonstrate YOLO detectors for track add Shandong Stick.
For three kinds of few situations of common training sample early period, model modification is caused not catch up with the variation of environment, interference Situation.At this moment using high confidence level update can improve tracking success rate to a certain extent, red algorithm failure after, yellow and Although green algorithm drifts about, still tracking.But since target deformation is happened at the former frames of video, Learning mistakes The model sample of Cheng Xunlian is very little, causes, once tracking fails, target just again to be can't detect using sliding window method.Using YOLO Detector approach is based on training pattern, less can be influenced by such case.
In conclusion the present invention relates to a kind of object tracking methods of view-based access control model, tracked with reference to YOLO detectors and KCF Wave filter can realize better familiar object tracking.Compared with prior art, it is an advantage of the invention that：High confidence level updates Tracking filter greatly reduces filter drift；Individual grader need not be trained to improve real-time during tracking；Fortune Row deep learning neutral net does object detection, obtains more accurate testing result, is influenced smaller by trace model drift, Can improve well run into when tracking for a long time block, disturb, training sample early period few, situations such as target quickly changes, Improve the robustness of tracking for a long time.
By explanation and attached drawing, the exemplary embodiments of the specific structure of specific embodiment are given, it is smart based on the present invention God can also make other conversions.Although foregoing invention proposes existing preferred embodiment, however, these contents are not intended as Limitation.
For a person skilled in the art, after reading above description, various changes and modifications undoubtedly will be evident. Therefore, appended claims should regard whole variations and modifications of the true intention and scope that cover the present invention as.It is weighing The scope and content of any and all equivalence, are all considered as still belonging to the intent and scope of the invention in the range of sharp claim.

Claims (9)

1. a kind of object tracking methods of view-based access control model, which is characterized in that include the following steps：
S1：Subscriber frame runs tracking process after selecting target to be tracked, calculates the peak sidelobe ratio PSR during tracking in real time, when When PSR is more than threshold tau 1, starts KCF tracking and KCF detection process, tracking result and correlation filter model are updated, when PSR exists When between τ 1 and τ 2, tracking result is only updated, does not update correlation filter model, when PSR is less than τ 2, do not performed and tracked Journey runs detection module；
S2：, tracking during train individual grader be used for detection module；
S3：YOLO detectors are run during detection, detect in image institute it is possible that comprising 20 kinds of common objects as further The input of detection；
S4：Testing result and tracking filter model are made into multiplication cross, when result is more than threshold value P, tracking result is updated, and stops Operation detector.
2. the object tracking methods of a kind of view-based access control model according to claim 1, which is characterized in that the KCF was tracked Journey includes the following steps：
S11, in It frames, sampled near the pt of current location, training one recurrence device, it is described recurrence device for calculate it is one small The response of window sample；
S12, in It+1 frames, sampled near previous frame position pt, judge the response each sampled with the recurrence device in S11；
The most strong sampling of S13, response is as this frame position pt+1.
A kind of 3. object tracking methods of view-based access control model according to claim 1, which is characterized in that the operation in the S3 YOLO detectors, specifically comprise the following steps：
When S31, tracking failure, YOLO target detection processes are activated；
Output after S32, detection is the result is that output boundary frame, the XY coordinates including bounding box rectangle, wide height, the value of the confidence and Generic etc..
S33, according to testing result, obtains several roughing results；
S34, roughing result is carried out selected, obtains unique maximum response, be final detection target.
A kind of 4. object tracking methods of view-based access control model according to any one of claim 1 to 3, which is characterized in that institute Object tracking methods are stated on Macbook Air 1.7GHz i7 processors, it is right using Cmake+OpenCV development platforms OTB100 sequences are emulated.
A kind of 5. object tracking methods of view-based access control model according to any one of claim 1 to 3, which is characterized in that institute It states object tracking methods and Xcode+OpenCV development platforms is being based on mobile phone, trace routine real-time Transmission target frame position is given Three axis realize the tracing control function to target from steady holder.
A kind of 6. object tracking methods of view-based access control model according to any one of claim 1 to 3, which is characterized in that institute Object tracking methods are stated to can be used for tracking a variety of different types of objects.
A kind of 7. object tracking methods of view-based access control model according to claim 6, which is characterized in that the object tracking side Method can be used for the object of 20 kinds or more of tracking.
A kind of 8. object tracking methods of view-based access control model according to any one of claim 1 to 3, which is characterized in that institute Object tracking methods are stated to apply on mobile phone.
A kind of 9. object tracking methods of view-based access control model according to claim 8, which is characterized in that the object tracking side Method is applied on the mobile phone of hand-held holder.
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