CN109360226A - A kind of multi-object tracking method based on time series multiple features fusion - Google Patents

A kind of multi-object tracking method based on time series multiple features fusion Download PDF

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CN109360226A
CN109360226A CN201811210852.8A CN201811210852A CN109360226A CN 109360226 A CN109360226 A CN 109360226A CN 201811210852 A CN201811210852 A CN 201811210852A CN 109360226 A CN109360226 A CN 109360226A
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tracking
target
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田胜
陈丽琼
邹炼
范赐恩
杨烨
胡雨涵
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Wuhan University WHU
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Abstract

The invention proposes a kind of multi-object tracking methods based on time series multiple features fusion.The method of the present invention obtains the classification and candidate frame of tracking target according to multi-target detection algorithm；Moving projection central point is calculated using convolutional network and correlation filter and screens candidate frame；Calculate appearance similarity scores；Calculate kinematic similarity score；Calculate interaction feature similarity scores；Candidate frame updates tracking clarification of objective information in the conversion of the tracking box of current frame image after being screened；Calculate the moving projection central point and screening candidate frame of the tracking target for not being matched to candidate frame；Not matched candidate frame is associated for already present tracking target, constructs new tracking target；Using the degree of overlapping handed over and between tracking target more each than calculating；It is the target to have disappeared by the tracking Target in multiple image continuously in loss state.Compared with prior art, the present invention improves tracking accuracy.

Description

A kind of multi-object tracking method based on time series multiple features fusion
Technical field
The present invention relates to computer visions, target following technical field, how special based on time series more particularly to one kind Levy the multi-object tracking method of fusion.
Background technique
Target following refers in image sequence, first detects the interested target of system, and it is accurately fixed to carry out to target Then the motion information of target is constantly updated in position during target is mobile, to realize the lasting tracking to target.Target Tracking can be divided into multiple target tracking and monotrack, and monotrack only focuses on an interesting target, and task is design One motion model or display model, which solve the factors such as change of scale, target occlusion, illumination, to be influenced, and is calibrated frame by frame interested The corresponding picture position of target.Compared to monotrack, multiple target tracking also needs to solve two additional tasks: discovery is simultaneously Handle the target of emerging target and disappearance in video sequence；Maintain the specific identity of each target.
Track target initialization, frequently block, target leaves detection zone, the similar appearance of multiple targets, Yi Jiduo Interaction between a target all can increase difficulty to multiple target tracking.In order to judge the mesh of emerging target and disappearance in time Mark, multiple target tracking algorithm generally require the basis that multi-target detection is realized as algorithm.
In recent years, with the development of deep learning, the development of computer vision field is very rapid.Algorithm of target detection is Through very accurate, and processing speed with higher.But in multiple target tracking field, since the difficult point of multiple target tracking is not yet complete Complete solution is determined, and the data association algorithm based on detection still has very big room for promotion.The innovation of the invention consists in that using phase The position that filtering algorithm predicts each target is closed, the dependency degree of detection algorithm is reduced, while proposing one based on object position It sets, appearance, movement, LSTM (the Long Short-Term Memory) network frame for interacting multiple features, passes through and extract high distinguish The characteristic model of degree overcomes multiple target occlusion issue, improves the precision of multiple target tracking.
Currently, the mode of multiple target tracking field more prevalence is to rely on the data association algorithm of detector, such side Method has well solved the problems such as object initialization, extinction and change of scale, but still cannot solve to depend on detection unduly very well Mutually blocked between device performance, multiple target, similar appearance target distinguish the problems such as.
Summary of the invention
In order to solve the above-mentioned technical problem, the invention proposes a kind of more mesh based on time series multiple features data correlation Mark tracking.
The technical scheme is that a kind of multi-object tracking method based on time series multiple features data correlation, specifically The following steps are included:
Step 1: according to the tracking target in SSD multi-target detection algorithm detection frame image, passing through SSD detecting and tracking target Confidence level compared with confidence threshold value, the classification of statistical trace target and the candidate frame for tracking target；
Step 2: extracting tracking target in the convolution feature of the position frame of present frame, by tracking target using convolutional network Correlation filter calculate current frame image in each position response confidence score, the point of highest scoring is defined as currently The moving projection central point of target, and the candidate frame screened by moving projection central point are tracked under frame image；
Step 3: calculating in tracking state or lose the appearance similarity scores of candidate frame after the tracking target and screening of state；
Step 4: calculating in tracking state or lose the kinematic similarity score of candidate frame after the tracking target and screening of state；
Step 5: calculating in tracking state or lose the interaction feature similitude of candidate frame after the tracking target and screening of state Score；
Step 6: if in tracking state or if losing the tracking object matching to candidate frame of state by total similarity scores and Matching score threshold compares, and when total similarity scores are greater than matching score threshold, then candidate frame is converted to tracking target in present frame The tracking box of image updates external appearance characteristic, the velocity characteristic, interaction feature information of tracking target；If in tracking state or losing The tracking target forgotten oneself is not matched to candidate frame, then the status information of tracking target is updated by step 2；
Step 7: not matched candidate frame is associated for already present tracking target, it will not matched candidate frame identification Newly to track target, new tracking target is initialized, new tracking target is established, constructs the position feature model, outer of new tracking target See characteristic model, velocity characteristic model and interaction feature model, and its state be updated to tracking state, in subsequent frame image into Row data correlation matched jamming；
Step 8: again retrieve present frame each tracking target in tracking mode, using hand over and than calculating it is each with Degree of overlapping between track target；
Step 9: being the target to have disappeared by the tracking Target in continuous multiple frames image continuously in loss state, protect The data information of its tracking mode is deposited, Data Matching operation no longer is carried out to it.
Preferably, frame image described in step 1 is m width image, the categorical measure that target is tracked described in step 1 is Nm, the candidate frame of target is tracked described in step 1 are as follows:
Di,m={ xi,m∈[li,m,li,m+lenthi,m],yi,m∈[wi,m,wi,m+widthi,m]|(xi,m,yi,m)},i∈[1, Km]
Wherein, KmFor the candidate frame quantity for tracking target in m frame image, li,mFor the i-th tracking in m frame image The starting point coordinate of the candidate frame X-axis of target, wi,mFor in m frame image i-th tracking target candidate frame Y-axis starting point coordinate, lenthi,mFor the length of the candidate frame of the i-th tracking target in m frame image, widthi,mFor the i-th tracking in m frame image The width of the candidate frame of target；
Preferably, convolutional network described in step 2 is the VGG16 network good in ImageNet classification task pre-training, And the first layer feature vector of tracking position of object frame is extracted by VGG16 network；
Pass through the two-dimensional feature vector of channel cInterpolation model by the two-dimensional feature vector of channel cIt is converted into one-dimensional The feature vector of continuous space:
Wherein,For the two-dimensional feature vector of channel c, bcIt is defined as a cube interpolating function three times, NcForAdopt Sample number, L are the length of the feature vector of one-dimensional continuous space, and Channel is the quantity in channel；
Convolution operator are as follows:
Wherein, yi,mIt is the response of the tracking target i of m image,For the two-dimensional feature vector of channel c, Channel For the quantity in channel,The feature vector of the one-dimensional continuous space of channel c,It is tracking target i in m frame image The correlation filter of channel c；
Pass through training sample training correlation filter are as follows:
In n given training sample to { (yi,q,y'i,q) be trained to obtain under (q ∈ [m-n, m-1]), that is, pass through It minimizes objective function optimization and obtains correlation filter:
Wherein, yi,m-jIt is the response of the tracking target i of m-j image, y'i,m-jFor yi,m-jIdeal Gaussian distribution,Correlation filter for tracking target i in the channel c of m frame image, weight αjIt is the impact factor of training sample j, by punishing Penalty function w is determined, obtains the correlation filter in each channel by training
Pass through the response y of the tracking target i of m imagei,m(l) l ∈ [0, L), maximizing yi,m(l) corresponding lp,i,m:
lp,i,m=argmax (yi,m(l))l∈[0,L)
Wherein, L is the length of the feature vector of one-dimensional continuous space；
By lp,i,mIt is converted into the point of the two-dimensional feature vector in channelAfter being reduced to two-dimensional coordinate, it is mapped as Coordinate points p under present framei,m=(xp,i,m,yp,i,m), the i-th tracking target T as in m frame imageiMoving projection center Point；
If tracking target TiIn tracking state, the candidate frame around predicted location area is only selected to carry out subsequent number of targets According to matching:
If tracking target TiPrevious frame length be lenthi,m-1, width widthi,m-1, the i-th tracking in m frame image Target TiMoving projection central point be pi,m=(xp,i,m,yp,i,m), the candidate frame center of the i-th tracking target in m frame image Point is ci,m=(li,m+lenthi,m/2,wi,m+widthi,m/2)i∈[1,Km], when candidate frame central point and moving projection center The distance of point meets condition:
d(pi,m,ci,m)=(xp,i,m-li,m-lenthi,m/2)2+(yp,i,m-wi,m-widthi,m/2)2< min (lenthi,m-1/2,widthi,m-1/2)
The candidate frame for the condition that meets then is subjected to subsequent target data matching；
If tracking target TiIn state is lost, selection screens candidate frame near the position of its disappearance former frame:
Moving projection central point is t when taking its disappearance former framei,m=(xt,i,m,yt,i,m), length lenthi,m-1, width For widthi,m-1, when candidate frame center and disappearance centre distance d (ti,ci,m) when meeting following conditions:
d(ti,m,ci,m)=(xt,i,m-li,m-lenthi,m/2)2+(yt,i,m-wi,m-widthi,m/2)2< min (lenthi,m-1/2,widthi,m-1/2)
The candidate frame for the condition that meets then is subjected to subsequent target data matching；
If tracking target TiIn failed matched jamming state, moving projection central point can be used and update in its candidate frame Heart point:
Update tracking target TiCandidate frame central point be moving projection central point pi,m=(xp,i,m,yp,i,m), candidate frame Length, the width of candidate frame and m-1 frame image remain unchanged；
Preferably, candidate frame is the time screened by step 2 according to moving projection central point after screening described in step 3 Select frame；
Appearance similarity scores described in step 3 specifically calculates are as follows:
By described in step 2 in m frame image i-th tracking target screening after candidate frame Di,mMost by removal VGG16 The articulamentum VGG16 network of later layer obtains tracking target T in the m frame image of N-dimensionaliExternal appearance characteristic vector
It is trained and is respectively obtained with training method end to end to training set by multiple target tracking public data collection The LSTM network of external appearance characteristic and and the first full articulamentum FC1；
Target T will be trackediPreceding M frame image data pass through it is same by remove VGG16 the last layer articulamentum VGG16 network extracts the LSTM network for further passing through external appearance characteristic after the external appearance characteristic vector of M N-dimensional, extracts N-dimensional United history external appearance characteristic vector
Joint connectionWithBy the first full articulamentum FC1, to obtain tracking target TiWith candidate frame Di,m Appearance similarity scores SA(Ti,Di,m), if target TiCertain preceding frame image data not yet generate, then with 0 value replacement；
Preferably, kinematic similarity score described in step 4 calculates are as follows:
Described in step 2 in m frame image i-th tracking target screening after candidate frame Di,mCentral point are as follows:
(li,m+lenthi,m/2,wi,m+widthi,m/2)
Previous frame image tracks target TiCandidate frame center are as follows:
(li,m-1+lenthi,m-1/2,wi,m-1+widthi,m-1/2)
The velocity characteristic vector of i-th tracking target in m frame image are as follows:
It is trained and is respectively obtained with training method end to end to training set by multiple target tracking public data collection The LSTM network of velocity characteristic and and the second full articulamentum FC2；
The LSTM network that the velocity characteristic vector of i-th tracking target in M frame image is passed through to velocity characteristic, extracts joint The motion feature vector of historical series
Joint connectionWithBy the second full articulamentum FC2, thus the tracking mesh in tracking state or loss state Mark TiWith candidate frame Di,mKinematic similarity score be SV(Ti,Di,m), if target TiCertain preceding frame exercise data not yet produce It is raw, then with the replacement of 0 value；
Preferably, interaction feature similarity scores described in step 5 calculates are as follows:
With candidate frame D after screeningi,mCentre coordinate ci,m=(li,m+lenthi,m/2,wi,m+widthi,m/ 2) centered on, The box for establishing the fixed size that length and width are H, by frame with other candidate frame centre coordinate ci',mThe point of coincidence is set to 1, Gu The box center for determining size is also set to 1, remaining position is 0, obtains:
Wherein,
x∈[li,m+lenthi,m/2-H/2,li,m+lenthi,m/2+H/2]
y∈[wi,m+widthi,m/2-H/2,wi,m+widthi,m/2+H/2]
Again willBeing converted into length is H2One-dimensional vector, the interaction feature vector for obtaining candidate frame is
It is trained and is respectively obtained with training method end to end to training set by multiple target tracking public data collection The LSTM network of interaction feature and and the full articulamentum FC3 of third；
With target TiCentered on the centre coordinate of certain frame image, the box for the fixed size that length and width are H is established, it will be in frame Be set to 1 with other points for being overlapped of tracking target's center's coordinates, the box center of fixed size is also set to 1, remaining position is 0, Obtain target TiIn the interaction feature vector of the frame, by target TiPreceding M frame number interaction feature vector by interaction feature LSTM network extracts united history interaction feature vector
JointWithBy the full articulamentum FC3 of third, to obtain TiAnd Di,mInteraction feature similarity scores SI (Ti,Di,m), if target TiCertain preceding frame interaction feature vector not yet generate, then with 0 value replacement；
Preferably, total similarity scores described in step 6 are as follows:
Stotal,i=α1SA(Ti,Di,m)+α2SV(Ti,Di,m)+α3SI(Ti,Di,m)
Wherein, α1For external appearance characteristic likeness coefficient, α2For velocity characteristic likeness coefficient, α3For interaction feature similitude Coefficient；
Total similarity scores are greater than matching score threshold Stotal,i> β then candidate frame Di,mTracking target is converted in m frame figure The tracking box of picture；
In step 6 by step 2 update tracking target status information be keep tracking target be tracking state, for continuous The failed matched tracking target in tracking state of multiframe, then be translated into loss state, no longer using side described in step 2 Method；
Preferably, the degree of overlapping between each tracking target described in step 8 are as follows:
Wherein, A is tracking target TaTracking box area, B be tracking target TbTracking box area, for be in IOU > 0.8 tracking target TaWith tracking target Tb, according to the obtained total similarity scores S of the step 6total,aWith Stotal,bInto Row compares, by Stotal,aWith Stotal,bLower tracking targeted transformation is to lose state, keeps Stotal,aWith Stotal,bHigher tracking Target is tracking state；
Preferably, multiple image described in step 9 is MDFrame.
Compared with prior art, the present invention has the following advantages and beneficial effects:
Characteristic of the method for the present invention according to each target under time series, constructs LSTM network frame, so that System can solve the problem of target is blocked for a long time, preferably improve the matched standard of target data in conjunction with historical data feature Exactness；
Present invention incorporates the features in terms of the position of tracking target, appearance, movement, interaction four, have used convolution net Network extracts the appearance further feature information and shallow-layer characteristic information of object, improves the discrimination of tracking target signature；With object The direction of the every frame movement of body and velocity information, have in continuity speciality from object of which movement information, improve object matching accuracy； By the interaction feature information of object under successive frame, interaction models are proposed, analyze tracking target and the other targets of surrounding Active force relationship, to improve matching accuracy.It is improved by using the mode that multi thread combines progress Data Matching The accuracy of target following；
Quick correlation filtering autotracking method is used to each target, calculates the position that target moves under present frame It sets, filters out the candidate frame for meeting the band of position, reduce the calculation amount of data association algorithm well.Autotracking algorithm for The tracking state target of missing inspection in target detection can solve the problems, such as to depend on object detector performance unduly from line trace.
Detailed description of the invention
Fig. 1: technical solution of the present invention the general frame；
Fig. 2: the survival condition figure of single target；
Fig. 3: external appearance characteristic Model Matching figure；
Fig. 4: velocity characteristic Model Matching figure；
Fig. 5: interaction feature Model Matching figure；
Fig. 6: interaction feature LSTM network model matching figure；
Fig. 7: system multiple target tracking schematic diagram.
Specific embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, with reference to the accompanying drawings and embodiments to this hair It is bright to be described in further detail, it should be understood that implementation example described herein is merely to illustrate and explain the present invention, not For limiting the present invention.
The embodiment of the present invention is introduced below with reference to Fig. 1 to Fig. 6.The technical solution of the present embodiment is a kind of based on time sequence The multi-object tracking method of column multiple features data correlation, specifically includes the following steps:
Step 1: according to the tracking target in SSD multi-target detection algorithm detection frame image, passing through SSD detecting and tracking target Confidence level compared with confidence threshold value, the classification of statistical trace target and the candidate frame for tracking target；
Frame image described in step 1 is m width image, and the categorical measure that target is tracked described in step 1 is Nm, step 1 Described in track target candidate frame are as follows:
Di,m={ xi,m∈[li,m,li,m+lenthi,m],yi,m∈[wi,m,wi,m+widthi,m]|(xi,m,yi,m)},i∈[1, Km]
Wherein, KmFor the candidate frame quantity for tracking target in m frame image, li,mFor the i-th tracking in m frame image The starting point coordinate of the candidate frame X-axis of target, wi,mFor in m frame image i-th tracking target candidate frame Y-axis starting point coordinate, lenthi,mFor the length of the candidate frame of the i-th tracking target in m frame image, widthi,mFor the i-th tracking in m frame image The width of the candidate frame of target；
Step 2: extracting tracking target in the convolution feature of the position frame of present frame, by tracking target using convolutional network Correlation filter calculate current frame image in each position response confidence score, the point of highest scoring is defined as currently The moving projection central point of target, and the candidate frame screened by moving projection central point are tracked under frame image；
Convolutional network described in step 2 is the VGG16 network good in ImageNet classification task pre-training, and is passed through The first layer feature vector of VGG16 network extraction tracking position of object frame；
Pass through the two-dimensional feature vector of channel cInterpolation model by the two-dimensional feature vector of channel cIt is converted into one-dimensional The feature vector of continuous space:
Wherein,For the two-dimensional feature vector of channel c, bcIt is defined as a cube interpolating function three times, NcForSampling Number, L are the length of the feature vector of one-dimensional continuous space, and Channel=512 is the quantity in channel；
Convolution operator are as follows:
Wherein, yi,mIt is the response of the tracking target i of m image,For the two-dimensional feature vector of channel c, Channel For the quantity in channel,The feature vector of the one-dimensional continuous space of channel c,It is tracking target i in m frame image The correlation filter of channel c；
Pass through training sample training correlation filter are as follows:
In n given training sample to { (yi,q,y'i,q) be trained to obtain under (q ∈ [m-n, m-1]), that is, pass through It minimizes objective function optimization and obtains correlation filter:
Wherein, yi,m-jIt is the response of the tracking target i of m-j image, y'i,m-jFor yi,m-jIdeal Gaussian distribution,Correlation filter for tracking target i in the channel c of m frame image, weight αjIt is the impact factor of training sample j, by punishing Penalty function w is determined, obtains the correlation filter in each channel by trainingNumber of training n=30；
Pass through the response y of the tracking target i of m imagei,m(l) l ∈ [0, L), maximizing yi,m(l) corresponding lp,i,m:
lp,i,m=argmax (yi,m(l))l∈[0,L)
Wherein, L is the length of the feature vector of one-dimensional continuous space；
By lp,i,mIt is converted into the point of the two-dimensional feature vector in channelAfter being reduced to two-dimensional coordinate, it is mapped as Coordinate points p under present framei,m=(xp,i,m,yp,i,m), the i-th tracking target T as in m frame imageiMoving projection center Point；
If tracking target TiIn tracking state, the candidate frame around predicted location area is only selected to carry out subsequent number of targets According to matching:
If tracking target TiPrevious frame length be lenthi,m-1, width widthi,m-1, the i-th tracking in m frame image Target TiMoving projection central point be pi,m=(xp,i,m,yp,i,m), the candidate frame center of the i-th tracking target in m frame image Point is ci,m=(li,m+lenthi,m/2,wi,m+widthi,m/2)i∈[1,Km], when candidate frame central point and moving projection center The distance of point meets condition:
d(pi,m,ci,m)=(xp,i,m-li,m-lenthi,m/2)2+(yp,i,m-wi,m-widthi,m/2)2< min (lenthi,m-1/2,widthi,m-1/2)
The candidate frame for the condition that meets then is subjected to subsequent target data matching；
If tracking target TiIn state is lost, selection screens candidate frame near the position of its disappearance former frame:
Moving projection central point is t when taking its disappearance former framei,m=(xt,i,m,yt,i,m), length lenthi,m-1, width For widthi,m-1, when candidate frame center and disappearance centre distance d (ti,ci,m) when meeting following conditions:
d(ti,m,ci,m)=(xt,i,m-li,m-lenthi,m/2)2+(yt,i,m-wi,m-widthi,m/2)2< min (lenthi,m-1/2,widthi,m-1/2)
The candidate frame for the condition that meets then is subjected to subsequent target data matching；
If tracking target TiIn failed matched jamming state, moving projection central point can be used and update in its candidate frame Heart point:
Update tracking target TiCandidate frame central point be moving projection central point pi,m=(xp,i,m,yp,i,m), candidate frame Length, the width of candidate frame and m-1 frame image remain unchanged；
Step 3: calculating in tracking state or lose the appearance similarity scores of candidate frame after the tracking target and screening of state；
Candidate frame is the candidate frame screened by step 2 according to moving projection central point after screening described in step 3；
Appearance similarity scores described in step 3 specifically calculates are as follows:
By described in step 1 in m frame image i-th tracking target candidate frame Di,mBy removing VGG16 the last layer Articulamentum VGG16 network, obtain N=1000 dimension m frame image in track target TiExternal appearance characteristic vector
By multiple target tracking the given training set of public data collection MOT17-Challenge with training method end to end into Row training respectively obtains the LSTM network and and the first full articulamentum FC1 of external appearance characteristic；
Target T will be trackediPreceding M frame image data pass through it is same by remove VGG16 the last layer articulamentum VGG16 network extracts the LSTM network for further passing through external appearance characteristic after the external appearance characteristic vector of M N-dimensional, extracts N-dimensional United history external appearance characteristic vector
Joint connectionWithBy the first full articulamentum FC1, to obtain tracking target TiWith candidate frame Di,m Appearance similarity scores SA(Ti,Di,m), if target TiCertain preceding frame image data not yet generate, then with 0 value replacement；
Step 4: calculating in tracking state or lose the tracking target of state and the kinematic similarity score of candidate frame；
Kinematic similarity score described in step 4 calculates are as follows:
Described in step 2 in m frame image i-th tracking object filtering after candidate frame Di,mCentral point are as follows:
(li,m+lenthi,m/2,wi,m+widthi,m/2)
Previous frame image tracks target TiCandidate frame center are as follows:
(li,m-1+lenthi,m-1/2,wi,m-1+widthi,m-1/2)
The velocity characteristic vector of i-th tracking target in m frame image are as follows:
By multiple target tracking the given training set of public data collection MOT17-Challenge with training method end to end into Row training respectively obtains the LSTM network and and the second full articulamentum FC2 of velocity characteristic；
The LSTM network that the velocity characteristic vector of i-th tracking target in M frame image is passed through to velocity characteristic, extracts joint The motion feature vector of historical series
Joint connectionWithBy the second full articulamentum FC2, thus the tracking mesh in tracking state or loss state Mark TiWith candidate frame Di,mKinematic similarity score be SV(Ti,Di,m), if target TiCertain preceding frame exercise data not yet produce It is raw, then with the replacement of 0 value；
Step 5: calculating in tracking state or lose the tracking target of state and the interaction feature similarity scores of candidate frame；
Interaction feature similarity scores described in step 5 calculates are as follows:
With candidate frame D after screeningi,mCentre coordinate ci,m=(li,m+lenthi,m/2,wi,m+widthi,m/ 2) centered on, The box for establishing the fixed size that length and width are H, by frame with other candidate frame centre coordinate ci',mThe point of coincidence is set to 1, Gu The box center for determining size is also set to 1, remaining position is 0, obtains:
Wherein,
x∈[li,m+lenthi,m/2-H/2,li,m+lenthi,m/2+H/2]
y∈[wi,m+widthi,m/2-H/2,wi,m+widthi,m/2+H/2]
Again willBeing converted into length is H2One-dimensional vector, the interaction feature vector for obtaining candidate frame is
By multiple target tracking the given training set of public data collection MOT17-Challenge with training method end to end into Row training respectively obtains the LSTM network and and the full articulamentum FC3 of third of interaction feature；
With target TiCentered on the centre coordinate of certain frame image, the box for the fixed size that length and width are H=300 is established, The point being overlapped with other tracking target's center's coordinates in frame is set to 1, the box center of fixed size is also set to 1, remaining position It is set to 0, obtains target TiIn the interaction feature vector of the frame, by target TiPreceding M frame number interaction feature vector it is special by interaction The LSTM network of sign extracts united history interaction feature vector
JointWithBy the full articulamentum FC3 of third, to obtain TiAnd Di,mInteraction feature similarity scores SI (Ti,Di,m), if target TiCertain preceding frame interaction feature vector not yet generate, then with 0 value replacement；
Step 6: if in tracking state or if losing the tracking object matching to candidate frame of state by total similarity scores and Matching score threshold compares, and when total similarity scores are greater than matching score threshold, then candidate frame is converted to tracking target in present frame The tracking box of image updates external appearance characteristic, the velocity characteristic, interaction feature information of tracking target；If in tracking state or losing The tracking target forgotten oneself is not matched to candidate frame, then the status information of tracking target is updated by step 2；
Total similarity scores described in step 6 are as follows:
Stotal,i=α1SA(Ti,Di,m)+α2SV(Ti,Di,m)+α3SI(Ti,Di,m)
Wherein, α1For external appearance characteristic likeness coefficient, α2For velocity characteristic likeness coefficient, α3For interaction feature similitude Coefficient；
Total similarity scores are greater than matching score threshold Stotal,i> β then candidate frame Di,mTracking target is converted in m frame figure The tracking box of picture；
In step 6 by step 2 update tracking target status information be keep tracking target be tracking state, for continuous The failed matched tracking target in tracking state of multiframe, then be translated into loss state, no longer using side described in step 2 Method；
Step 7: not matched candidate frame is associated for already present tracking target, it will not matched candidate frame identification Newly to track target, new tracking target is initialized, new tracking target is established, constructs the position feature model, outer of new tracking target See characteristic model, velocity characteristic model and interaction feature model, and its state be updated to tracking state, in subsequent frame image into Row data correlation matched jamming；
Step 8: again retrieve present frame each tracking target in tracking mode, using hand over and than calculating it is each with Degree of overlapping between track target；
Degree of overlapping between each tracking target described in step 8 are as follows:
Wherein, A is tracking target TaTracking box area, B be tracking target TbTracking box area, for be in IOU > 0.8 tracking target TaWith tracking target Tb, according to the obtained total similarity scores S of the step 6total,aWith Stotal,bInto Row compares, by Stotal,aWith Stotal,bLower tracking targeted transformation is to lose state, keeps Stotal,aWith Stotal,bHigher tracking Target is tracking state；
Step 9: being the target to have disappeared by the tracking Target in multiple image continuously in loss state, save it The data information of tracking mode no longer carries out Data Matching operation to it.
Multiple image described in step 9 is MD=30 frame images.
It should be understood that the part that this specification does not elaborate belongs to the prior art.
It should be understood that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this The limitation of invention patent protection range, those skilled in the art under the inspiration of the present invention, are not departing from power of the present invention Benefit requires to make replacement or deformation under protected ambit, fall within the scope of protection of the present invention, this hair It is bright range is claimed to be determined by the appended claims.

Claims (9)

1. a kind of multi-object tracking method based on time series multiple features fusion, which comprises the following steps:
Step 1: according to the tracking target in SSD multi-target detection algorithm detection frame image, passing through setting for SSD detecting and tracking target Reliability is compared with confidence threshold value, the classification of statistical trace target and the candidate frame for tracking target；
Step 2: extracting tracking target in the convolution feature of the position frame of present frame, by the phase for tracking target using convolutional network The response confidence score that filter calculates each position in current frame image is closed, the point of highest scoring is defined as present frame figure As the moving projection central point of lower tracking target, and the candidate frame screened by moving projection central point；
Step 3: calculating in tracking state or lose the appearance similarity scores of candidate frame after the tracking target and screening of state；
Step 4: calculating in tracking state or lose the kinematic similarity score of candidate frame after the tracking target and screening of state；
Step 5: calculating in tracking state or lose the interaction feature similarity scores of candidate frame after the tracking target and screening of state；
Step 6: if in tracking state or lose state tracking object matching to candidate frame if by total similarity scores with match Score threshold compares, and when total similarity scores are greater than matching score threshold, then candidate frame is converted to tracking target in current frame image Tracking box, update tracking target external appearance characteristic, velocity characteristic, interaction feature information；If in tracking state or losing state Tracking target be not matched to candidate frame, then by step 2 update tracking target status information；
Step 7: not matched candidate frame being associated for already present tracking target, not matched candidate frame is regarded as newly Target is tracked, new tracking target is initialized, establishes new tracking target, position feature model, the appearance for constructing new tracking target are special Model, velocity characteristic model and interaction feature model are levied, and its state is updated to tracking state, is counted in subsequent frame image According to association matched jamming；
Step 8: each tracking target in tracking mode of present frame is retrieved again, using friendship and than calculating each tracking mesh Degree of overlapping between mark；
Step 9: being the target to have disappeared by the tracking Target in continuous multiple frames image continuously in loss state, save it The data information of tracking mode no longer carries out Data Matching operation to it.
2. the multi-object tracking method according to claim 1 based on time series multiple features fusion, it is characterised in that: step Frame image described in rapid 1 is m width image, and the categorical measure that target is tracked described in step 1 is Nm, tracked described in step 1 The candidate frame of target are as follows:
Di,m={ xi,m∈[li,m,li,m+lenthi,m],yi,m∈[wi,m,wi,m+widthi,m]|(xi,m,yi,m)},i∈[1,Km]
Wherein, KmFor the candidate frame quantity for tracking target in m frame image, li,mFor the i-th tracking target in m frame image The starting point coordinate of candidate frame X-axis, wi,mFor in m frame image i-th tracking target candidate frame Y-axis starting point coordinate, lenthi,mFor the length of the candidate frame of the i-th tracking target in m frame image, widthi,mFor the i-th tracking in m frame image The width of the candidate frame of target.
3. the multi-object tracking method according to claim 1 based on time series multiple features fusion, it is characterised in that: step Convolutional network described in rapid 2 is the VGG16 network good in ImageNet classification task pre-training, and is extracted by VGG16 network The first layer feature vector of tracking position of object frame；
Pass through the two-dimensional feature vector of channel cInterpolation model by the two-dimensional feature vector of channel cIt is converted into one-dimensional continuous The feature vector in space:
Wherein,For the two-dimensional feature vector of channel c, bcIt is defined as a cube interpolating function three times, NcForHits, L For the length of the feature vector of one-dimensional continuous space, Channel is the quantity in channel；
Convolution operator are as follows:
Wherein, yi,mIt is the response of the tracking target i of m image,For the two-dimensional feature vector of channel c, Channel is logical The quantity in road,The feature vector of the one-dimensional continuous space of channel c,It is to track target i in the channel c of m frame image Correlation filter；
Pass through training sample training correlation filter are as follows:
In n given training sample to { (yi,q,y'i,q) be trained to obtain under (q ∈ [m-n, m-1]), that is, pass through minimum Change objective function optimization and obtain correlation filter:
Wherein, yi,m-jIt is the response of the tracking target i of m-j image, y'i,m-jFor yi,m-jIdeal Gaussian distribution,For with Correlation filter of the track target i in the channel c of m frame image, weight αjIt is the impact factor of training sample j, by penalty w It determines, the correlation filter in each channel is obtained by training
Pass through the response y of the tracking target i of m imagei,m(l) l ∈ [0, L), maximizing yi,m(l) corresponding lp,i,m:
lp,i,m=argmax (yi,m(l))l∈[0,L)
Wherein, L is the length of the feature vector of one-dimensional continuous space；
By lp,i,mIt is converted into the point of the two-dimensional feature vector in channelAfter being reduced to two-dimensional coordinate, it is mapped as present frame Under coordinate points pi,m=(xp,i,m,yp,i,m), the i-th tracking target T as in m frame imageiMoving projection central point；
If tracking target TiIn tracking state, the candidate frame around predicted location area is only selected to carry out subsequent target data Match:
If tracking target TiPrevious frame length be lenthi,m-1, width widthi,m-1, the i-th tracking target in m frame image TiMoving projection central point be pi,m=(xp,i,m,yp,i,m), the candidate frame central point of the i-th tracking target is in m frame image ci,m=(li,m+lenthi,m/2,wi,m+widthi,m/2)i∈[1,Km], when candidate frame central point and moving projection central point Distance meets condition:
d(pi,m,ci,m)=(xp,i,m-li,m-lenthi,m/2)2+(yp,i,m-wi,m-widthi,m/2)2< min (lenthi,m-1/2, widthi,m-1/2)
The candidate frame for the condition that meets then is subjected to subsequent target data matching；
If tracking target TiIn state is lost, selection screens candidate frame near the position of its disappearance former frame:
Moving projection central point is t when taking its disappearance former framei,m=(xt,i,m,yt,i,m), length lenthi,m-1, width is widthi,m-1, when candidate frame center and disappearance centre distance d (ti,ci,m) when meeting following conditions:
d(ti,m,ci,m)=(xt,i,m-li,m-lenthi,m/2)2+(yt,i,m-wi,m-widthi,m/2)2< min (lenthi,m-1/2, widthi,m-1/2)
The candidate frame for the condition that meets then is subjected to subsequent target data matching；
If tracking target TiIn failed matched jamming state, moving projection central point can be used and update its candidate frame central point:
Update tracking target TiCandidate frame central point be moving projection central point pi,m=(xp,i,m,yp,i,m), the length of candidate frame Degree, the width of candidate frame and m-1 frame image remain unchanged.
4. the multi-object tracking method according to claim 1 based on time series multiple features fusion, it is characterised in that: step Candidate frame is the candidate frame screened by step 2 according to moving projection central point after screening described in rapid 3；
Appearance similarity scores described in step 3 specifically calculates are as follows:
By described in step 2 in m frame image i-th tracking target screening after candidate frame Di,mBy removal VGG16 last The articulamentum VGG16 network of layer obtains tracking target T in the m frame image of N-dimensionaliExternal appearance characteristic vector
It is trained by multiple target tracking public data collection to training set with training method end to end and respectively obtains appearance The LSTM network of feature and and the first full articulamentum FC1；
Target T will be trackediPreceding M frame image data pass through it is same by remove VGG16 the last layer articulamentum VGG16 net Network extracts the LSTM network for further passing through external appearance characteristic after the external appearance characteristic vector of M N-dimensional, extracts N-dimensional and go through in combination History external appearance characteristic vector
Joint connectionWithBy the first full articulamentum FC1, to obtain tracking target TiWith candidate frame Di,mIt is outer See similarity scores SA(Ti,Di,m), if target TiCertain preceding frame image data not yet generate, then with 0 value replacement.
5. the multi-object tracking method according to claim 1 based on time series multiple features fusion, it is characterised in that: step Kinematic similarity score described in rapid 4 calculates are as follows:
Described in step 2 in m frame image i-th tracking target screening after candidate frame Di,mCentral point are as follows:
(li,m+lenthi,m/2,wi,m+widthi,m/2)
Previous frame image tracks target TiCandidate frame center are as follows:
(li,m-1+lenthi,m-1/2,wi,m-1+widthi,m-1/2)
The velocity characteristic vector of i-th tracking target in m frame image are as follows:
It is trained by multiple target tracking public data collection to training set with training method end to end and respectively obtains speed The LSTM network of feature and and the second full articulamentum FC2；
The LSTM network that the velocity characteristic vector of i-th tracking target in M frame image is passed through to velocity characteristic, extracts joint history The motion feature vector of sequence
Joint connectionWithBy the second full articulamentum FC2, thus the tracking target T in tracking state or loss stateiWith Candidate frame Di,mKinematic similarity score be SV(Ti,Di,m), if target TiCertain preceding frame exercise data not yet generate, then with 0 Value replaces.
6. the multi-object tracking method according to claim 1 based on time series multiple features fusion, it is characterised in that: step Interaction feature similarity scores described in rapid 5 calculate are as follows:
With candidate frame D after screeningi,mCentre coordinate ci,m=(li,m+lenthi,m/2,wi,m+widthi,m/ 2) it centered on, establishes Length and width are the box of the fixed size of H, by frame with other candidate frame centre coordinate ci',mThe point of coincidence is set to 1, fixes big Small box center is also set to 1, remaining position is 0, obtains:
Wherein,
x∈[li,m+lenthi,m/2-H/2,li,m+lenthi,m/2+H/2]
y∈[wi,m+widthi,m/2-H/2,wi,m+widthi,m/2+H/2]
Again willBeing converted into length is H2One-dimensional vector, the interaction feature vector for obtaining candidate frame is
It is trained by multiple target tracking public data collection to training set with training method end to end and respectively obtains interaction The LSTM network of feature and and the full articulamentum FC3 of third；
With target TiCentered on the centre coordinate of certain frame image, establish the box for the fixed size that length and width are H, by frame with The point that other tracking target's center's coordinates are overlapped is set to 1, and the box center of fixed size is also set to 1, remaining position is 0, obtains Target TiIn the interaction feature vector of the frame, by target TiThe interaction feature vector of preceding M frame number pass through the LSTM net of interaction feature Network extracts united history interaction feature vector
JointWithBy the full articulamentum FC3 of third, to obtain TiAnd Di,mInteraction feature similarity scores SI(Ti, Di,m), if target TiCertain preceding frame interaction feature vector not yet generate, then with 0 value replacement.
7. the multi-object tracking method according to claim 1 based on time series multiple features fusion, it is characterised in that: step Total similarity scores described in rapid 6 are as follows:
Stotal,i=α1SA(Ti,Di,m)+α2SV(Ti,Di,m)+α3SI(Ti,Di,m)
Wherein, α1For external appearance characteristic likeness coefficient, α2For velocity characteristic likeness coefficient, α3For interaction feature likeness coefficient；
Total similarity scores are greater than matching score threshold Stotal,i> β then candidate frame Di,mTracking target is converted in m frame image Tracking box；
In step 6 by step 2 update tracking target status information be keep tracking target be tracking state, for continuous multiple frames The failed matched tracking target in tracking state, then be translated into loss state, no longer use step 2 the method.
8. the multi-object tracking method according to claim 1 based on time series multiple features fusion, it is characterised in that: step Degree of overlapping between each tracking target described in rapid 8 are as follows:
Wherein, A is tracking target TaTracking box area, B be tracking target TbTracking box area, for be in IOU > 0.8 Tracking target TaWith tracking target Tb, according to the obtained total similarity scores S of the step 6total,aWith Stotal,bCompared Compared with by Stotal,aWith Stotal,bLower tracking targeted transformation is to lose state, keeps Stotal,aWith Stotal,bHigher tracking target To track state.
9. the multi-object tracking method according to claim 1 based on time series multiple features fusion, it is characterised in that: step Multiple image described in rapid 9 is MDFrame.
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