CN109800624A - A kind of multi-object tracking method identified again based on pedestrian - Google Patents
A kind of multi-object tracking method identified again based on pedestrian Download PDFInfo
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Abstract
The invention discloses a kind of multi-object tracking methods identified again based on pedestrian, comprising: establishes simultaneously off-line training pedestrian weight identification model；From adjacent two field pictures, target feature vector M and N are extracted respectively；Calculate the final similarity matrix between target feature vector M and N；Optimum Matching path is solved using Kuhn-Munkres algorithm, obtains the Optimum Matching between target feature vector M and N；Online updating pedestrian weight identification model parameter.Distinguishing characteristics between target and background can not only effectively be identified using the present invention, when multiple similar targets occur, additionally it is possible to identify the distinguishing characteristic between multiple targets, and then Rapid matching target, realize accurately tracking for multiple target.
Description
Technical field
The present invention relates to Artificial smart field, in particular to a kind of multiple target tracking side identified again based on pedestrian
Method.
Background technique
In video image processing, target tracking algorism is very important.
After obtaining the motion profile of target in the picture by target following, for subsequent progress target number statistics, mesh
Mark behavioural analysis etc. significantly.
The target that we need to track under normal circumstances is behaved and vehicle.
Since vehicle target is relatively large, and it is rigid objects, is relatively easy so tracking is got up, and for pedestrian
Tracking, due to attitudes vibration, illumination variation, partial occlusion, the reasons such as similar interference of background, are always a problem.
Multitarget Tracking, it is current for gradually developing from the Generative models of early stage
Discriminative models method.
The Generative models theme of early stage is exactly to one feature of Objective extraction, then in subsequent view
Characteristic matching is carried out in frequency sequence, and then determines the position of target, and representative algorithm has NCC, SSD, SAD etc..But it is this
Algorithm have the shortcomings that one it is obvious, only considered the similarity of target and oneself, do not account for the similarity with background.Work as target
With background in texture and slightly close color, it is easy to losing target.
Present target tracking algorism is substantially replaced Discriminative models algorithm.Core concept is online
Learn the feature for having discrimination with background out.Typical algorithm has TLD, KCF etc..As soon as this kind of algorithm also has a distinct disadvantage,
It is the discrimination only considered with background, does not account for the discrimination between target, that is to say, that when multiple similar targets occur,
Tracking target is easy to error hiding.
Recently, deep learning is propagated its belief on a large scale, and occurs extracting target signature using deep learning model, so carry out with
The algorithm of track.For example a series of observations are obtained first with fasterRCNN SSD detection algorithm, then exist
Feature is extracted on the corresponding position of featuremap as target signature, this feature only has the discrimination between classification, do not have
The discrimination of standby individual.Be utilized the CNN convolution feature of multiple image there are also some algorithms, but this feature just with
The multiframe information of target itself, being still unable to get between target has the feature of discrimination.
Based on this, this paper presents the algorithms tracked using pedestrian's weight identification feature.
Summary of the invention
Object of the present invention is to: propose a kind of multi-object tracking method identified again based on pedestrian, creative for the first time says
Pedestrian's weight identification technology and Multitarget Tracking are fused together, and greatly improve the effect of tracking.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of multi-object tracking method identified again based on pedestrian, is included the following steps:
S1, pedestrian's weight identification model, and off-line training pedestrian weight identification model are established；
S2, assume that adjacent two field pictures are respectively A and B, and the target number in A image is M, the mesh in B image
Mark number be it is N number of, then using pedestrian weight identification model, from adjacent two field pictures, respectively extract target feature vector M and N；
S3, using the COS distance between target feature vector M and N as characteristic similarity, and according to its COS distance meter
Calculate the similarity matrix SM and Distance matrix D M between target feature vector M and N；
S4, similarity matrix SM is weighted using Distance matrix D M, is obtained between target feature vector M and N most
Whole similarity matrix；
S5, using final similarity matrix as similarity weight between target feature vector M and N, by target feature vector M
The tracking problem of multiple target between N is abstracted as the Optimum Matching problem of cum rights bipartite graph, then utilizes Kuhn-Munkres
Algorithm solves Optimum Matching path, obtains the Optimum Matching between target feature vector M and N；
S6, online updating pedestrian weight identification model parameter.
Further, the foundation of pedestrian's weight identification model and off-line training method include the following steps:
S11, selection caffe training frame；
S12, it is to meet requirement of real-time, using inception model, on the basis of inceptionV2, eliminates
All layers of inception4 and inception5 only retain inception3a, inception3b and inception3c layer,
And port number is kept to original half, it is adjusted on imageNet data set sorter network；
S13, public data collection and private data collection are collected, acquires video data and using based on ART artificial neural network
Detecting and tracking algorithm obtain the image sequence of each target；
S14, the manual intervention in the result of the detecting and tracking algorithm carry out wrong rejecting, obtain final target figure
As sequence；
S15, on the basis of public data collection trained model, adjust again, obtain final pedestrian's weight identification model.
Further, the specific preparation method of the image sequence of each target is as follows: being detected using Faster RCNN
Pedestrian, and tracking target is initialized, it is then persistently tracked using KCF algorithm, and then obtain the image sequence of same target.
Further, the online updating pedestrian weight identification model parameter includes the following steps:
The stable target of one S61, selection tracking；
S62, after determining the stable target of tracking, as anchor, other tenacious trackings in present frame are then traversed
Target, and according to the cosine similarity of two feature vectors, find out the clarification of objective similarity highest stable with the tracking
One be used as negative；Consistent with the ID number of an anchor and minimum characteristic similarity conduct in traversal past N frame
positive；
S63, after tri- features of anchor, negative and positive determine, using the ternary in recognition of face
Group loss function carries out gradient anti-pass to model, and is only updated to the weight of the last layer feature extraction layer of model.
Further, the stable target of the tracking need to meet following two points requirement when being selected:
A, when using openpose system detection human body key point, it is necessary to have more than 85% or more key point be detected
It arrives；
B, and the lap of other targets has to be lower than 0.3；It is ensured that so not by other target occlusions, is extracted
To the feature for being characterized in target itself.
The beneficial effects of the present invention are: can not only effectively identify that the difference between target and background is special using the present invention
Sign, when multiple similar targets occur, additionally it is possible to identify the distinguishing characteristic between multiple targets, and then Rapid matching target,
Realize accurately tracking for multiple target.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Specific embodiment
Below in conjunction with attached drawing, the present invention will be further described.
The present invention is creative for the first time to be fused together pedestrian's weight identification technology and Multitarget Tracking, and then significantly
Improve the accuracy of multiple target tracking.
Pedestrian mentioned in the present invention identifies that also referred to as pedestrian identifies (Personre-identification) again again, is
The technology that whether there is specific pedestrian in image or video sequence is judged using computer vision technique, is an image retrieval
Subproblem, the feature that learns is precisely to be directed to discrimination between pedestrian's individual, is just used as target by us
The feature of tracking.It is compared with the feature of former algorithm, more prior distribution information is utilized, obtain the feature distribution of pedestrian,
So that tracking is more stable.
It is illustrated below with a specific embodiment:
1, pedestrian's weight identification model, and off-line training pedestrian weight identification model are established.
Pedestrian's weight identification model training based on deep learning, it is thus necessary to determine that three elements, training frame, training network knot
Structure and training data.
Training frame, select caffe training frame here.It is primarily due to inside the Zoo of caffe there are much openings
Network model and corresponding pre-training model, are adjusted on this basis convenient for us.
Training network structure, in order to meet requirement of real-time, we use improved inception model,
On the basis of inceptionV2, all layers of inception4 and inception5 are eliminated, reservation inception3a,
Inception3b and inception3c layers, and channels is kept to original half, classify in imageNet data set
It is adjusted on network, needs to illustrate that a little what it is due to here is improved inception network model, so needing
Multistep adjustment is carried out on the basis of original imageNet training pattern, not so have the risk of drift.
Training data is collected, and is collected open source data, including Market-1501, MSMT17 of public data collection etc. and is collected
Private data collection.In the video of acquisition, the image sequence that each target is obtained based on the detecting and tracking algorithm of ART algorithm is utilized
Column, for example pedestrian is detected using Faster RCNN, and initialize tracking target, it is then persistently tracked, is obtained using KCF algorithm
To the image sequence of same target.In order to guarantee accuracy, manual intervention carries out wrong rejecting in the result of track algorithm, obtains
It is adjusted again to final target image sequence, and then on the basis of public data collection trained model, obtains final row
People's weight identification model.
2, it is tracked using the feature that pedestrian's weight identification model is extracted
There is the good feature for having discrimination, the design of track algorithm is just very simple, for example we use graph-
The Kuhn-Munkres algorithm of min-cut, so that it may the Optimum Matching between target is obtained, in order to obtain better effect, I
The image pixel distance known pedestrian again between the distance between another characteristic and target comprehensively consider, feeding figure cuts algorithm,
Obtain the final matching relationship of target.
Concretely: assuming that adjacent two field pictures are respectively A and B, and the target number in A image is M, B image
In target number be it is N number of, then using pedestrian weight identification model, from adjacent two field pictures, respectively extract target signature to
Measure M and N；Using the COS distance between target feature vector M and N as characteristic similarity, and calculated according to its COS distance
Similarity matrix SM and Distance matrix D M between target feature vector M and N；Using Distance matrix D M to similarity matrix SM into
Row weighting, obtains the final similarity matrix between target feature vector M and N；Using final similarity matrix as target signature
The tracking problem of multiple target between target feature vector M and N is abstracted as cum rights two and divided by similarity weight between vector M and N
The Optimum Matching problem of figure, then using Kuhn-Munkres algorithm solve Optimum Matching path, obtain target feature vector M and
Optimum Matching between N；
3, online updating pedestrian weight identification model parameter
In order to enable the model of training has better robustness, we devise the algorithm of online updating model parameter, make
Obtaining pedestrian's weight identification model has better generalization ability, better adaptability.In view of factors such as calculation amounts, we do not allow institute
There is tracked target to be involved in the update of model, but passes through certain policy selection partial target.Specific step is as follows:
The stable target of one S61, selection tracking only results in model and gets over if target inherently tracks mistake
It is poorer to learn, so a good evaluation criterion is critically important, fixed standard is as follows herein, and first, people is detected by openpose
Body key point, it is necessary to which the key point more than 85% or more is detected；The second and overlap of other targets has to be lower than
0.3, the feature that by other target occlusions, do not extract and be characterized in target itself is ensured that in this way；
S62, after determining a tracking stable objects, as anchor, then traverse other in present frame stablize with
Track target, searches out and the clarification of objective similarity highest one as negative, traversal N frame (such as N=in the past
50) ID number for neutralizing anchor is consistent, the minimum conduct positive of characteristic similarity；
After S63, three features determine, using the triple loss function in recognition of face, the gradient for carrying out model is anti-
It passes, in order to save calculation amount, only the weight of the last layer feature extraction layer of model is updated here.
Specific implementation process of the invention is as shown in Figure 1:
S1, pedestrian's weight identification model, and off-line training pedestrian weight identification model are established；
S2, assume that adjacent two field pictures are respectively A and B, and the target number in A image is M, the mesh in B image
Mark number be it is N number of, then using pedestrian weight identification model, from adjacent two field pictures, respectively extract target feature vector M and N；
S3, using the COS distance between target feature vector M and N as characteristic similarity, and according to its COS distance meter
Calculate the similarity matrix SM and Distance matrix D M between target feature vector M and N；
S4, similarity matrix SM is weighted using Distance matrix D M, is obtained between target feature vector M and N most
Whole similarity matrix；
S5, using final similarity matrix as similarity weight between target feature vector M and N, by target feature vector M
The tracking problem of multiple target between N is abstracted as the Optimum Matching problem of cum rights bipartite graph, then utilizes Kuhn-Munkres
Algorithm solves Optimum Matching path, obtains the Optimum Matching between target feature vector M and N；
S6, online updating pedestrian weight identification model parameter.
The advantages of basic principles and main features and this programme of this programme have been shown and described above.The technology of the industry
Personnel are it should be appreciated that this programme is not restricted to the described embodiments, and the above embodiments and description only describe this
The principle of scheme, under the premise of not departing from this programme spirit and scope, this programme be will also have various changes and improvements, these changes
Change and improvement is both fallen within the scope of claimed this programme.This programme be claimed range by appended claims and its
Equivalent thereof.
Claims (5)
1. a kind of multi-object tracking method identified based on pedestrian again, which comprises the steps of:
S1, pedestrian's weight identification model, and off-line training pedestrian weight identification model are established；
S2, assume that adjacent two field pictures are respectively A and B, and the target number in A image is M, the target in B image
It is N number of for counting, then extracts target feature vector M and N respectively from adjacent two field pictures using pedestrian's weight identification model；
S3, using the COS distance between target feature vector M and N as characteristic similarity, and calculated according to its COS distance
Similarity matrix SM and Distance matrix D M between target feature vector M and N；
S4, similarity matrix SM is weighted using Distance matrix D M, obtains the most last phase between target feature vector M and N
Like degree matrix；
S5, using final similarity matrix as similarity weight between target feature vector M and N, by target feature vector M and N
Between the tracking problem of multiple target be abstracted as the Optimum Matching problem of cum rights bipartite graph, then utilize Kuhn-Munkres algorithm
Optimum Matching path is solved, the Optimum Matching between target feature vector M and N is obtained；
S6, online updating pedestrian weight identification model parameter.
2. a kind of multi-object tracking method identified based on pedestrian as described in claim 1 again, which is characterized in that the pedestrian
The foundation of weight identification model and off-line training method include the following steps:
S11, selection caffe training frame；
S12, it is to meet requirement of real-time, using inception model, on the basis of inceptionV2, eliminates
All layers of inception4 and inception5 only retain inception3a, inception3b and inception3c layer,
And port number is kept to original half, it is adjusted on imageNet data set sorter network；
S13, public data collection and private data collection are collected, acquire video data and utilizes the inspection based on ART artificial neural network
It surveys track algorithm and obtains the image sequence of each target；
S14, the manual intervention in the result of the detecting and tracking algorithm carry out wrong rejecting, obtain final target image sequence
Column；
S15, on the basis of public data collection trained model, adjust again, obtain final pedestrian's weight identification model.
3. a kind of multi-object tracking method identified based on pedestrian as claimed in claim 2 again, which is characterized in that described each
The specific preparation method of the image sequence of target is as follows: pedestrian detected using Faster RCNN, and initializes tracking target,
Then it is persistently tracked using KCF algorithm, and then obtains the image sequence of same target.
4. a kind of multi-object tracking method identified based on pedestrian as described in claim 1 again, which is characterized in that described online
Pedestrian's weight identification model parameter is updated to include the following steps:
The stable target of one S61, selection tracking；
S62, after determining the stable target of tracking, as anchor, other tenacious tracking targets in present frame are then traversed,
And according to the cosine similarity of two feature vectors, clarification of objective similarity highest one stable with the tracking is found out
As negative；Consistent with the ID number of an anchor and minimum characteristic similarity conduct in traversal past N frame
positive；
S63, after tri- features of anchor, negative and positive determine, damaged using the triple in recognition of face
Function is lost, gradient anti-pass is carried out to model, and be only updated to the weight of the last layer feature extraction layer of model.
5. a kind of multi-object tracking method identified based on pedestrian as claimed in claim 4 again, which is characterized in that the tracking
Stable target need to meet following two points requirement when being selected:
A, when using openpose system detection human body key point, it is necessary to have more than 85% or more key point and be detected；
B, and the lap of other targets has to be lower than 0.3.
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