CN109255339A - Classification method based on adaptive depth forest body gait energy diagram - Google Patents

Classification method based on adaptive depth forest body gait energy diagram Download PDF

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CN109255339A
CN109255339A CN201811222012.3A CN201811222012A CN109255339A CN 109255339 A CN109255339 A CN 109255339A CN 201811222012 A CN201811222012 A CN 201811222012A CN 109255339 A CN109255339 A CN 109255339A
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forest
sample
adaptive
current iteration
depth
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CN109255339B (en
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赵盼盼
盛立杰
苗启广
马悦
庞博
秦丹
陈红颖
徐劲夫
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Xidian Univ
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00335Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
    • G06K9/00342Recognition of whole body movements, e.g. for sport training
    • G06K9/00348Recognition of walking or running movements, e.g. gait recognition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/004Artificial life, i.e. computers simulating life
    • G06N3/006Artificial life, i.e. computers simulating life based on simulated virtual individual or collective life forms, e.g. single "avatar", social simulations, virtual worlds or particle swarm optimisation

Abstract

The invention discloses a kind of classification methods based on adaptive depth forest body gait energy diagram, the steps include: (1) building training set and test set;(2) adaptive depth forest model is constructed;(3) nicety of grading of adaptive depth forest model is calculated;(4) judge whether adaptive depth forest model reaches requirement;(5) if training set accuracy rate and depth not up to require, extend adaptive depth forest model, it solves customized secondary convex optimization problem and obtains adaptive depth forest model in the Enhanced feature of training set, the training set of adaptive depth forest after being expanded by the Enhanced feature on training set;(6) test set classification accuracy rate is calculated.The present invention reduces the complexities of model and model to need the weight of training, while reducing gait energy diagram sample size required for training pattern.

Description

Classification method based on adaptive depth forest body gait energy diagram
Technical field
The invention belongs to technical field of image processing, further relate to one of image identification technical field and are based on certainly Adapt to the body gait energy diagram classification method of depth forest.The present invention can be used for body gait energy diagram GEI (Gait Energy Image) in gait feature classify.
Background technique
Gait Recognition technology is that a kind of biotechnology of identification is carried out according to the posture that people in video sequence walks.By In Gait Recognition have the characteristics that non-infringement property, remote identity and be difficult to it is hiding, therefore Gait Recognition country it is public The fields such as safety, financial security, authentication, video monitoring have a wide range of applications.
A kind of patent document " gait knowledge based on deep learning of scientific and technological (Beijing) Co., Ltd of milky way water droplet in its application It is proposed in other method " (number of patent application: 201410587758X, application publication number: CN104299012A) a kind of based on depth The gait recognition method of study.This method describes gait sequence using gait energy diagram, is instructed by depth convolutional neural networks Practice Matching Model, to match the identity of Gait Recognition people.The training process of this method are as follows: to marked good identity be related to it is more The training gait video sequence at a visual angle extracts gait energy diagram, repeats to choose any two of them to based on convolutional neural networks Matching Model be trained until model restrain;The identification process of this method are as follows: to single-view gait video to be identified and Registration gait video sequence extracts gait energy diagram respectively, utilizes based on convolutional neural networks trained in training process The gait energy diagram and the registered each gait energy diagram of gait video sequence of single-view gait video to be identified are calculated with model Similarity, the size according to similarity carries out identity prediction, and exports recognition result.This method is to the gait across large viewing Variation has very strong robustness, and it is not high to efficiently solve existing gait Recognition technology precision when handling across visual angle Gait Recognition The problem of.But the shortcoming that this method still has is, since this method is the classification based on depth convolutional neural networks Method, and a large amount of training sample is needed based on the classification method of depth convolutional neural networks, it is fewer in training sample amount In the case of, the classification method of depth convolutional neural networks cannot sufficiently learn the gait feature in body gait energy diagram, thus The ability in feature extraction of depth convolutional neural networks is reduced, this is not suitable for the classification method of depth convolutional neural networks The data of small sample, and the classification performance based on depth convolutional neural networks depends critically upon parameter testing process, trains It needs to consume biggish computing resource in journey.
Inst. of Computing Techn. Academia Sinica is (special in the patent document " gait identification method and system " of its application Sharp application number: CN201710136803.3, application publication number: CN107016346A) in a kind of gait recognition method is provided.The party Method extracts behavioural characteristic, position feature and gait feature from the acceleration information that the intelligent terminal carried by user obtains;Benefit User's current behavior is identified according to the behavioural characteristic with preparatory trained Activity recognition model;Using in advance it is trained Position identification model identifies the present bit of the intelligent terminal according to the position feature and the user's current behavior identified It sets;Using preparatory trained Gait Recognition model according to the gait feature, the user's current behavior and the intelligence that are identified Can the current location of terminal identify the identity of user.This method improves the knowledge of gait identity by being layered progressive identification method Other accuracy rate and robustness, and do not need to limit the position and direction that related sensor is put, it is very flexibly and square Just it uses.But the shortcoming that this method still has is, adds since this method needs user to carry intelligent terminal to obtain Speed data extracts behavioural characteristic, position feature and the gait feature of user using the acceleration information of acquisition, this is weakened The non-infringement of Approach for Gait Classification, and this method is by preparatory trained Activity recognition model, position identification model and gait Feature extraction, can not all be dissolved into a model, cannot achieve and divide end to end by three model compositions of identification model Class.
Summary of the invention
It is a kind of based on adaptive depth forest people it is an object of the invention in view of the above shortcomings of the prior art, propose The classification method of body gait energy diagram.For the present invention compared with other existing Approach for Gait Classification, adaptive depth forest model is logical The weight for solving each tree is crossed, the Enhanced feature of adaptive depth forest model is obtained, it is adaptive with the Enhanced feature training of acquisition Depth forest model is answered, keeps weight required for training less, training speed is fast.In the training process according to test set and training The accuracy of collection is continuously increased the depth of adaptive depth forest, make the complexity of adaptive depth forest with training process from It adapts to adjust, reduces the complexity of model, take full advantage of computing resource.
The thinking for realizing the object of the invention is, first to all gait Video Composition gait energy diagrams, to utilize random combine The method of sampling obtains training set and test set.Then an initial adaptive depth forest model is constructed, and to model Parameter is initialized.The nicety of grading of the adaptive depth forest model of iteration before calculating.Judge the adaptive of current iteration Whether the nicety of grading on training set of depth forest model meets the requirements, the deconditioning if meeting the requirements, this model is The weight of each tree in depth forest is calculated, the adaptive depth of current iteration is obtained if being unsatisfactory for requiring for required model Enhanced feature of the forest model on test set is spent, if the accuracy rate on test set reaches requirement, then with regard to deconditioning, this Model is required model, is otherwise extended to current depth forest, and the training set Enhanced feature of depth forest is obtained, The training set of current depth forest is updated, the depth of depth forest is increased, and continues to be trained current depth forest, until Model required for obtaining.
The specific steps of the present invention are as follows:
(1) sample set is constructed:
(1a) carries out background modeling and background real-time update to 124 target person videos of input, obtains each target The human body contour outline foreground image of personage;
(1b) extracts body gait figure from the human body contour outline foreground image of each target person video;
(1c) is respectively by the gray value of all human body contour images of each target person obtained in each gait cycle After addition, then the sum of the human body contour outline image divided by each target person, obtain gait energy corresponding with the gait cycle Figure;
(1d) takes each target person under the conditions of normal walking and visual angle is 90 degree of six gait energy diagrams, and totally 744 It opens gait energy diagram and constitutes sample set;
(2) training set and test set are obtained:
(2a) carries out stochastical sampling to sample set, obtains 992 positive samples and negative sample using random combine sampling splicing method This, forms training set for all positive samples and negative sample;
(2b) carries out stochastical sampling to sample set, obtains 992 positive samples and negative sample using random combine sampling splicing method This, forms test set for all positive samples and negative sample;
(3) adaptive depth forest model is constructed:
(3a) builds the adaptive depth forest model being made of two random forests and two completely random forests;
(3b) by adaptive depth forest model two random forests and two completely random forests in the number set Be disposed as 400, the depth capacity of tree be disposed as 8, bootstrapping sample mark is disposed as True, and the nucleus number of parallel processing is all provided with It is set to nucleus number in the maximum of server, sets t for the depth of adaptive depth forest model;
(4) nicety of grading of the adaptive depth forest model of current iteration is calculated:
The training set of the adaptive depth forest model of current iteration is input to the adaptive depth of current iteration by (4a) In forest model, the corresponding class probability of each sample in training set is exported;
(4b) utilizes training set nicety of grading calculation formula, calculates adaptive depth forest of the training set Jing Guo current iteration The nicety of grading of adaptive depth forest model after model training;
(5) judge whether the nicety of grading of the adaptive depth forest model of current iteration is more than or equal to 95%;If so, holding Row step (12) otherwise executes step (6);
(6) weight of each tree in the adaptive depth forest model of current iteration is obtained:
(6a) utilizes following customized secondary convex optimizing expressions, obtains the adaptive depth forest model of current iteration The weight of each tree in each random forest:
ξi k>=0, i ∈ K
Wherein,ξ when minimizing target function type is sought in expressioni,wkOperation, i indicates in training set under sample Mark, K indicate that the sum of training set sample, ∑ indicate sum operation, ξi kIndicate in training set i-th sample current iteration from The basic loss in depth forest model in k-th of random forest is adapted to, λ indicates regularization coefficient, | | | | indicate 2 norms behaviour Make, wkIndicate total weight of k-th of forest in the adaptive depth forest model of current iteration, s.t. indicates constraint condition symbol Number, t indicates the subscript set in k-th of random forest in the adaptive depth forest model of current iteration, TkIndicate current iteration Adaptive depth forest model in the sum set in k-th of random forest, Pi (t,k)Indicate i-th of sample in current iteration Basic loss in adaptive depth forest model in t-th of tree of k-th of random forest, w(t,k)Indicate current iteration from Adapt to the t several weight of k-th of random forest in depth forest model, τ presentation class interval;
(6b) forms the weight of each tree in the adaptive each random forest of depth forest model of current iteration current The weight of the adaptive depth forest model of iteration;
(7) Enhanced feature of the adaptive depth forest model of current iteration on test set is obtained:
(7a) passes through the adaptive of current iteration according to the weight of each tree in the adaptive depth forest model of current iteration The Enhanced feature solution formula of depth forest model is answered, obtaining each sample in test set respectively is positive sample and negative sample Enhanced feature;
(7b) by test set the positive sample of all samples and the Enhanced feature of negative sample constitute the adaptive of current iteration Answer the Enhanced feature of depth forest model;
(8) accuracy of test set is calculated:
According to Enhanced feature of the adaptive depth forest model of current iteration on test set, using current iteration from Adapt to the accuracy that depth forest model test set accuracy calculation method calculates test set;
(9) judge whether the adaptive depth forest model test set accuracy of current iteration is being greater than the maximum of test set just True rate otherwise, executes step (12) if so, thening follow the steps (10);
(10) Enhanced feature of the adaptive depth forest model of current iteration on training set is obtained:
(10a) according to the weight of each tree in the adaptive depth forest model of current iteration, by current iteration from The Enhanced feature solution formula of depth forest model is adapted to, obtaining each sample in training set respectively is positive sample and negative sample Enhanced feature;
(10b) by training set the positive sample of all samples and the Enhanced feature of negative sample constitute the adaptive of current iteration Answer the Enhanced feature of depth forest model;
(11) the adaptive depth forest model of current iteration is extended:
(11a) is by the adaptive depth forest of the training set of the adaptive depth forest model of current iteration and current iteration The Enhanced feature head and the tail of model splice, and obtain the training set of the adaptive depth forest of current iteration;
(11b) is using the depth t+1 of the adaptive depth forest model of current iteration as the adaptive depth of current iteration The depth t of forest model;
(12) judge whether the depth t of the adaptive depth forest model of current iteration is greater than 100, if so, being instructed Step (13) are executed after the adaptive depth forest model perfected, otherwise, are executed step (4);
(13) classify to gait energy diagram all in test set:
The test set of the adaptive depth forest model of current iteration is input to trained adaptive depth forest mould In type, classify to gait energy diagram in test set.
The present invention compared with prior art, has the advantage that
First, since the present invention is by the weight of solution each tree, the Enhanced feature of adaptive depth forest model is obtained, Using the adaptive depth forest model of Enhanced feature training, overcomes the prior art and need to train during training neural network A large amount of weight, the not applicable problem of data set of the neural network model to small sample, so that the present invention has skilled weight The advantages of quantity is few, and training speed is fast, is suitable for small data set.
Second, since the present invention is continuously increased adaptive depth according to the accuracy of test set and training set in the training process The depth for spending forest makes the complexity of adaptive depth forest with training process automatic adjusument, overcomes prior art mind The problem of model complexity immobilizes in the training process through network, so that The present invention reduces the complexities of model, sufficiently Computing resource is utilized.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Specific embodiment
With reference to the accompanying drawing 1, the specific steps realized to the present invention are further described.
Step 1, sample set is constructed.
Background modeling and background real-time update are carried out to 124 target person videos of input, obtain each target person Human body contour outline foreground image.
The background modeling refers to background real-time update, and the previous frame of human target video is subtracted each other with background frames, is obtained To the human body contour outline foreground image of target person video;The power function in vision library is called, it will be in human body contour outline foreground image Existing cavity and the removal of scattered white point.
Body gait figure is extracted from the human body contour outline foreground image of each target person video.
The step of described extraction body gait figure is, by the point on each human body contour outline most left side and the point on the most right side away from From the width as human body contour outline, height of the uppermost point at a distance from nethermost point as human body contour outline;Utilize human body Profile width the characteristic sexually revised synchronizing cycle occurs at any time, gait is divided by the change width signal of human body contour outline Period;Human body contour outline image is deducted by the width and height of human body contour outline and keeps depth-width ratio constant, by all human body contour outlines Image scaling is to same size.
The gray value of all human body contour images of each target person obtained in each gait cycle is added respectively Afterwards, the sum of the human body contour outline image then divided by each target person obtains gait energy diagram corresponding with the gait cycle.
It takes each target person under the conditions of normal walking and visual angle is 90 degree of six gait energy diagrams, totally 744 steps State energy diagram constitutes sample set.
Step 2, training set and test set are obtained.
Using random combine sampling splicing method, stochastical sampling is carried out to sample set, obtains 992 positive samples and negative sample, All positive samples and negative sample are formed into training set.
Using random combine sampling splicing method, stochastical sampling is carried out to sample set, obtains 992 positive samples and negative sample, All positive samples and negative sample are formed into test set.
The step of random combine sampling splicing method, is as follows:
The first step randomly selects 6 gaits of the human target of a unselected mistake from 124 human targets of sample set Energy diagram is as currently processed object.
Second step randomly selects four pairs of gait energy diagrams from currently processed object, each pair of gait energy diagram is carried out horizontal To splicing, constitutes a positive sample and set 0 for its label, the positive sample quantity of each target person is 4.
Third step randomly selects a gait energy diagram from currently processed object.
4th step randomly selects 4 different target personages from sample set in addition to current goal personage respectively, from institute In respective 6 gait energy diagrams of the target person of selection, a gait energy diagram is randomly selected respectively, totally 4 gait energy Figure.
5th step, the 4 state energy diagrams obtained with the 4th step respectively with the gait energy diagram that third step obtains are two-by-two Pairing, constitutes 4 pairs of gait energy diagrams, each pair of gait energy diagram is carried out horizontally-spliced, constitutes a negative sample and by its label It is set as 1, the negative sample quantity of each human target is 4.
Step 6: being processed by currently processed object tag.
Step 7: to 124 target persons, if 6 gait energy diagrams of each target person be collectively labeled as it is processed, that 496 positive samples and 496 negative samples are obtained, i.e. 992 samples altogether, to 124 target persons, target person if it exists 6 gait energy diagrams of object are untreated to be finished, and the first step is executed.
Step 3, adaptive depth forest model is constructed.
Build the adaptive depth forest model being made of two random forests and two completely random forests.
By in adaptive depth forest model two random forests and two completely random forests in the number set be all provided with Be set to 400, the depth capacity of tree be disposed as 8, bootstrapping sample mark is disposed as True, and the nucleus number of parallel processing is disposed as The depth of adaptive depth forest model is set t by nucleus number in the maximum of server.
Step 4, the nicety of grading of the adaptive depth forest model of current iteration is calculated.
The training set of the adaptive depth forest model of current iteration is input to the adaptive depth forest of current iteration In model, the corresponding class probability of each sample in training set is exported.
Using training set nicety of grading calculation formula, adaptive depth forest model of the training set Jing Guo current iteration is calculated The nicety of grading of adaptive depth forest model after training.
Step 5, judge the nicety of grading of adaptive depth forest model of current iteration whether less than 95%;If so, holding Otherwise row step 6 executes step 12.
Step 6, the weight of each tree in the adaptive depth forest model of current iteration is obtained.
(6.1) following customized secondary convex optimizing expressions is utilized, the adaptive depth forest mould of current iteration is obtained The weight of each tree in each random forest of type:
ξi k>=0, i ∈ K
Wherein,ξ when minimizing target function type is sought in expressioni,wkOperation, i indicate training set in sample mark Number, K indicates that the sum of training set sample, ∑ indicate sum operation, ξi kIndicate in training set i-th sample current iteration from The basic loss in depth forest model in k-th of random forest is adapted to, λ indicates regularization coefficient, | | | | indicate 2 norms behaviour Make, wkIndicate total weight of k-th of forest in the adaptive depth forest model of current iteration, s.t. indicates constraint condition symbol Number, t indicates the subscript set in k-th of random forest in the adaptive depth forest model of current iteration, TkIndicate current iteration Adaptive depth forest model in the sum set in k-th of random forest, Pi (t,k)Indicate i-th of sample in current iteration Basic loss in adaptive depth forest model in t-th of tree of k-th of random forest, w(t,k)Indicate current iteration from Adapt to the t several weight of k-th of random forest in depth forest model, τ presentation class interval;
I-th of sample is in the adaptive depth forest model of current iteration in t-th of tree of k-th of random forest Basic loss expression formula are as follows:
Pi (t,k)=Zi*(Pi,0 (t,k)-Pi,1 (t,k))
Wherein, ZiIndicator variable is indicated, if the label of i-th of sample is 0, Z in training seti=-1, if in training set The label of i sample is 1, then Zi=1, * indicate multiplication operation, Pi,0 (t,k), Pi,1 (t,k)Indicate i-th of sample in current iteration Class probability in t-th of tree in k-th of random forest in adaptive depth forest model.
(6.2) weight of each tree in the adaptive each random forest of depth forest model of current iteration is formed current The weight of the adaptive depth forest model of iteration;
Step 7, Enhanced feature of the adaptive depth forest model of current iteration on test set is obtained.
According to the weight of each tree in the adaptive depth forest model of current iteration, pass through the adaptive depth of current iteration The Enhanced feature solution formula of forest model is spent, obtains the enhancing that each sample in test set is positive sample and negative sample respectively Feature.
By in test set the positive sample of all samples and the Enhanced feature of negative sample constitute the adaptive depth of current iteration Spend the Enhanced feature of forest model.
The Enhanced feature solution formula of the adaptive depth forest model of the current iteration is as follows:
Wherein, vi,0Indicate enhancing probability of i-th of sample for positive sample, p in training seti,0 (t,k)It indicates i-th in training set The probability of a sample positive sample in t-th of tree of k-th of random forest in the adaptive depth forest model of current iteration, vi,1Indicate enhancing probability of i-th of sample for negative sample, p in training seti,1 (t,k)Indicate that i-th of sample is current in training set In the adaptive depth forest model of iteration in t-th of tree of k-th of random forest negative sample probability.
Step 8, the accuracy of test set is calculated.
According to Enhanced feature of the adaptive depth forest model of current iteration on test set, using current iteration from Adapt to the accuracy that depth forest model test set accuracy calculation method calculates test set.
The adaptive depth forest model test set accuracy calculation method of the current iteration is as follows:
The first step, to each sample in test set, the Enhanced feature numerical value of positive sample is greater than the Enhanced feature of negative sample Value, and the label of sample is 0, then and sample classification is correct, otherwise sample classification mistake.
Second step, to each sample in test set, the Enhanced feature numerical value of positive sample is less than the Enhanced feature of negative sample Value, and the label of sample is 1, then and sample classification is correct, otherwise sample classification mistake.
Third step, statistical test concentrate the sum for the sample correctly classified.
4th step obtains adaptive with the sum for the sample correctly classified in test set divided by the sum of test set sample Answer the test set accuracy of depth forest model.
Step 9, judge whether the adaptive depth forest model test set accuracy of current iteration is greater than test set most Otherwise big accuracy, executes step 12 if so, thening follow the steps 10.
The maximum accuracy of the test set refer to test set depth be 1,2 ..., the adaptive depth forest model of t The maximum value of upper accuracy.
Step 10, Enhanced feature of the adaptive depth forest model of current iteration on training set is obtained.
According to the weight of each tree in the adaptive depth forest model of current iteration, pass through the adaptive depth of current iteration The Enhanced feature solution formula of forest model is spent, obtains the enhancing that each sample in training set is positive sample and negative sample respectively Feature.
By in training set the positive sample of all samples and the Enhanced feature of negative sample constitute the adaptive depth of current iteration Spend the Enhanced feature of forest model.
Step 11, the adaptive depth forest model of current iteration is extended.
By the adaptive depth forest model of the training set of the adaptive depth forest model of current iteration and current iteration Enhanced feature head and the tail splice, obtain the training set of the adaptive depth forest model of current iteration.
Using the depth t+1 of the adaptive depth forest model of current iteration as adaptive depth forest when current iteration The depth t of model.
Step 12, judge whether the depth t of the adaptive depth forest model of current iteration is greater than 100, if so, obtaining Step 13 is executed after trained adaptive depth forest model, otherwise, executes step 4.
Step 13, classify to gait energy diagram all in test set.
The test set of the adaptive depth forest model of current iteration is input to trained adaptive depth forest mould In type, classify to gait energy diagram in test set.
Effect of the invention is described further below with reference to emulation experiment:
1, emulation experiment condition:
The hardware platform of emulation experiment of the invention are as follows: Intel Core i7-8700K@3.70GHz CPU, 64GB RAM、NVIDIA Geforce GTX1080Ti GPU。
The software platform of emulation experiment of the invention are as follows: python2.7 and TensorFlow1.4.
Data used in emulation experiment of the invention are disclosed Gait Recognition data set Dataset B, including 124 targets The profile of personage's gait of human body under three kinds of walking conditions (usual terms wears overcoat, carries package condition) and 11 visual angles Sketch figure, this method synthesize the gait energy diagram of 124 target persons, and choose each target person at normal walking and visual angle For six gait energy diagrams under conditions of 90 degree, totally 744 gait energy diagrams constitute sample set, are sampled and are spliced with random combine Method carries out stochastical sampling, composing training collection and test set to sample set.
2, emulation content and interpretation of result:
In emulation experiment of the invention, first according to each of method synthesis Dataset B data library of the invention The gait energy diagram at different conditions of target person, and each target person is chosen under the conditions of normal walking and visual angle is 90 degree of six gait energy diagrams, totally 744 gait energy diagrams constitute sample set, are sampled splicing method with random combine, to sample Collection carries out stochastical sampling, forms training set and test set.Using the adaptive depth forest model of training set training, then with use Test sample is tested in trained adaptive depth forest model, obtains test set nicety of grading of the invention.
Emulation experiment of the invention is under identical data set, using the method for the present invention and prior art random forest The test set that (paper " Random Forests " 2001 " Mach.Learning " of Leo Breiman) constructs the present invention In gait energy diagram classify.
In order to verify the classifying quality of method and prior art random forest of the invention, using following formula, this is calculated separately The nicety of grading of invention and random forest, and compare.
The sample correctly classified in nicety of grading=test set/test set total sample number
1. two methods nicety of grading list of table
Emulation experiment method Random forest This method
Nicety of grading 75.43% 92.93%
The present invention and existing random forest technology classification accuracy computation are listed in table 1 respectively as a result, as seen from Table 1, Nicety of grading of the invention be 92.93%, this index be much higher than random forest classification method, it was demonstrated that the present invention can obtain than The nicety of grading of the higher gait energy diagram of random forest method.

Claims (8)

1. a kind of classification method based on adaptive depth forest body gait energy diagram, which is characterized in that solve adaptive deep The weight for spending each tree in forest model, obtains the Enhanced feature of adaptive depth forest model, according to depth forest model Depth adaptive depthkeeping degree forest model complexity really utilizes the Enhanced feature training depth forest for obtaining depth forest model Model;The specific steps of this method include the following:
(1) sample set is constructed:
(1a) carries out background modeling and background real-time update to 124 target person videos of input, obtains each target person Human body contour outline foreground image;
(1b) extracts body gait figure from the human body contour outline foreground image of each target person video;
The gray value of all human body contour images of each target person obtained in each gait cycle is added by (1c) respectively Afterwards, the sum of the human body contour outline image then divided by each target person obtains gait energy diagram corresponding with the gait cycle;
Six gait energy diagrams that (1d) takes each target person under the conditions of normal walking and visual angle is 90 degree, totally 744 walk State energy diagram constitutes sample set;
(2) training set and test set are obtained:
(2a) carries out stochastical sampling to sample set, obtains 992 positive samples and negative sample using random combine sampling splicing method, All positive samples and negative sample are formed into training set;
(2b) carries out stochastical sampling to sample set, obtains 992 positive samples and negative sample using random combine sampling splicing method, All positive samples and negative sample are formed into test set;
(3) adaptive depth forest model is constructed:
(3a) builds the adaptive depth forest model being made of two random forests and two completely random forests;
(3b) by adaptive depth forest model two random forests and two completely random forests in the number set be all provided with Be set to 400, the depth capacity of tree be disposed as 8, bootstrapping sample mark is disposed as True, and the nucleus number of parallel processing is disposed as The depth of adaptive depth forest model is set t by nucleus number in the maximum of server;
(4) nicety of grading of the adaptive depth forest model of current iteration is calculated:
The training set of the adaptive depth forest model of current iteration is input to the adaptive depth forest of current iteration by (4a) In model, the corresponding class probability of each sample in training set is exported;
(4b) utilizes training set nicety of grading calculation formula, calculates adaptive depth forest model of the training set Jing Guo current iteration The nicety of grading of depth forest model after training;
(5) judge the nicety of grading of adaptive depth forest model of current iteration whether less than 95%;If so, executing step (6), step (12) otherwise, are executed;
(6) weight of each tree in the adaptive depth forest model of current iteration is obtained:
(6a) utilizes following customized secondary convex optimizing expressions, and the adaptive depth forest model for obtaining current iteration is each The weight of each tree in random forest:
ξi k>=0, i ∈ K
Wherein,ξ when minimizing target function type is sought in expressioni,wkOperation, i indicate training set in sample label, K table Show that the sum of training set sample, ∑ indicate sum operation, ξi kIndicate that i-th of sample is in the adaptive depth of current iteration in training set The basic loss in forest model in k-th of random forest is spent, λ indicates regularization coefficient, | | | | indicate the operation of 2 norms, wk Indicate total weight of k-th of forest in the adaptive depth forest model of current iteration, s.t. indicates constraint condition symbol, t table Show the subscript set in k-th of random forest in the adaptive depth forest model of current iteration, TkIndicate the adaptive of current iteration Answer the sum set in k-th of random forest in depth forest model, Pi (t,k)Indicate i-th of sample in the adaptive of current iteration Basic loss in depth forest model in t-th of tree of k-th of random forest, w(t,k)Indicate the adaptive depth in current iteration Spend the t several weight of k-th of random forest in forest model, τ presentation class interval;
The weight of each tree in the adaptive each random forest of depth forest model of current iteration is formed current iteration by (6b) Adaptive depth forest model weight;
(7) Enhanced feature of the adaptive depth forest model of current iteration on test set is obtained:
(7a) passes through the adaptive depth of current iteration according to the weight of each tree in the adaptive depth forest model of current iteration The Enhanced feature solution formula of forest model is spent, obtains the enhancing that each sample in test set is positive sample and negative sample respectively Feature;
(7b) by test set the positive sample of all samples and the Enhanced feature of negative sample constitute the adaptive depth of current iteration Spend the Enhanced feature of model;
(8) accuracy of test set is calculated:
According to Enhanced feature of the adaptive depth forest model of current iteration on test set, the adaptive of current iteration is utilized The accuracy of depth forest model test set accuracy calculation method calculating test set;
(9) judge whether the adaptive depth forest model test set accuracy of current iteration is greater than the maximum of test set correctly Rate otherwise, executes step (12) if so, thening follow the steps (10);
(10) Enhanced feature of the adaptive depth forest model of current iteration on training set is obtained:
(10a) passes through the adaptive of current iteration according to the weight of each tree in the adaptive depth forest model of current iteration The Enhanced feature solution formula of depth forest model obtains the increasing that each sample in training set is positive sample and negative sample respectively Strong feature;
(10b) by training set the positive sample of all samples and the Enhanced feature of negative sample constitute the adaptive depth of current iteration Spend the Enhanced feature of forest model;
(11) the adaptive depth forest model of current iteration is extended:
(11a) is by the increasing of the training set of the adaptive depth forest model of current iteration and the adaptive depth model of current iteration Strong feature head and the tail splice, and obtain the training set of the adaptive depth forest model of current iteration;
(11b) is gloomy as adaptive depth when current iteration using the depth t+1 of the adaptive depth forest model of current iteration The depth t of woods model;
(12) judge whether the depth t of the adaptive depth forest model of current iteration is greater than 100, if so, being trained Depth forest model after execute step (13), otherwise, execute step (4);
(13) classify to gait energy diagram all in test set:
The test set of the adaptive depth forest model of current iteration is input in trained depth forest model, to test Gait energy diagram is concentrated to classify.
2. the classification method according to claim 1 based on adaptive depth forest body gait energy diagram, feature exist In: background modeling described in step (1a) refers to background real-time update, by the previous frame of human target video and background frames phase Subtract, obtains the human body contour outline foreground image of target person video;The power function in vision library is called, by human body contour outline foreground picture The cavity as present in and scattered white point remove.
3. the classification method according to claim 1 based on adaptive depth forest body gait energy diagram, feature exist In: the step of extraction body gait figure described in step (1b), is, by the point on each human body contour outline most left side and the most right side Width of the distance of point as human body contour outline, height of the uppermost point at a distance from nethermost point as human body contour outline;Benefit The characteristic sexually revised synchronizing cycle occurs at any time with the profile width of human body, is drawn by the change width signal of human body contour outline Divide gait cycle;Human body contour outline image is deducted by the width and height of human body contour outline and keeps depth-width ratio constant, by owner Body contour images zoom to same size.
4. the classification method according to claim 1 based on adaptive depth forest body gait energy diagram, feature exist It is as follows in the step of: random combine described in step (2) samples splicing method:
The first step randomly selects 6 gait energy of the human target of a unselected mistake from 124 human targets of sample set Figure is used as currently processed object;
Second step randomly selects four pairs of gait energy diagrams from currently processed object, and each pair of gait energy diagram is carried out lateral spelling It connects, constitute a positive sample and sets 0 for its label, the positive sample quantity of each target person is 4;
Third step randomly selects a gait energy diagram from currently processed object;
4th step randomly selects 4 different target personages from sample set in addition to current goal personage respectively, from selected Respective 6 gait energy diagrams of target person in, randomly select a gait energy diagram respectively, totally 4 gait energy diagrams;
5th step is matched two-by-two with 4 state energy diagrams that the gait energy diagram that third step obtains is obtained with the 4th step respectively, 4 pairs of gait energy diagrams are constituted, the progress of each pair of gait energy diagram is horizontally-spliced, it constitutes a negative sample and sets its label to 1, the negative sample quantity of each human target is 4;
Step 6: being processed by currently processed object tag;
Step 7: to 124 target persons, if 6 gait energy diagrams of each target person be collectively labeled as it is processed, then total 496 positive samples and 496 negative samples are obtained, i.e. 992 samples altogether, to 124 target persons, target person if it exists 6 gait energy diagrams are untreated to be finished, and the first step is executed.
5. the classification method according to claim 1 based on adaptive depth forest body gait energy diagram, feature exist In: the t of i-th of sample described in step (6a), k-th of random forest in the adaptive depth forest model of current iteration Basic loss expression formula in a tree are as follows:
Pi (t,k)=Zi*(Pi,0 (t,k)-Pi,1 (t,k))
Wherein, ZiIndicator variable is indicated, if the label of i-th of sample is 0, Z in training seti=-1, if i-th in training set The label of sample is 1, then Zi=1, * indicate multiplication operation, Pi,0 (t,k), Pi,1 (t,k)Indicate i-th sample current iteration from Adapt in depth forest model in k-th of random forest class probability in t-th of tree.
6. the classification method according to claim 1 based on adaptive depth forest body gait energy diagram, feature exist In: the Enhanced feature solution formula of the adaptive depth forest model of current iteration described in step (7a) is as follows:
Wherein, vi,0Indicate enhancing probability of i-th of sample for positive sample, p in training seti,0 (t,k)Indicate i-th of sample in training set The probability of this positive sample in t-th of tree of k-th of random forest in the adaptive depth forest model of current iteration, vi,1Table Show enhancing probability of i-th of sample for negative sample, p in training seti,1 (t,k)Indicate that i-th of sample is in current iteration in training set In adaptive depth forest model in t-th of tree of k-th of random forest negative sample probability.
7. the classification method according to claim 1 based on adaptive depth forest body gait energy diagram, feature exist In: the adaptive depth forest model test set accuracy calculation method of current iteration described in step (8) is as follows:
The first step, to each sample in test set, the Enhanced feature numerical value of positive sample is greater than the Enhanced feature value of negative sample, and And the label of sample is 0, then sample classification is correct, otherwise sample classification mistake;
Second step, to each sample in test set, the Enhanced feature numerical value of positive sample is less than the Enhanced feature value of negative sample, and And the label of sample is 1, then sample classification is correct, otherwise sample classification mistake;
Third step, statistical test concentrate the sum for the sample correctly classified;
4th step obtains depth forest with the sum for the sample correctly classified in test set divided by the sum of test set sample The test set accuracy of model.
8. the classification method according to claim 1 based on adaptive depth forest body gait energy diagram, feature exist Refer to that test set in depth is 1,2 in: the maximum accuracy of test set described in step (9) ..., the adaptive depth forest of t The maximum value of accuracy on model.
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