CN108171752A - A kind of sea ship video detection and tracking based on deep learning - Google Patents

A kind of sea ship video detection and tracking based on deep learning Download PDF

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CN108171752A
CN108171752A CN201711461818.3A CN201711461818A CN108171752A CN 108171752 A CN108171752 A CN 108171752A CN 201711461818 A CN201711461818 A CN 201711461818A CN 108171752 A CN108171752 A CN 108171752A
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王德全
赵世凯
王小勇
陈坚松
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Chengdu Apq Polytron Technologies Inc
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/20—Processor architectures; Processor configuration, e.g. pipelining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30232—Surveillance

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Abstract

The invention discloses a kind of sea ship video detection and tracking based on deep learning, (1) convolution model feature extractions：It is learned automatically using depth convolutional neural networks and takes target ship feature, each characteristic layer goes extraction feature using a series of 3x3 convolution kernels, and the convolutional layer of shallow-layer mainly extracts minutia, and the convolutional layer of deep layer mainly extracts abstract global contour feature；(2) multiple dimensioned full convolution model target positioning and classification：The big measure feature obtained using trunk convolutional network, is reintegrated in multiple dimensioned convolutional layer, constructs the target signature pyramid network that scale successively decreases, each layer represents different size of target signature, fusion comparison is carried out between characteristic layer；(3) frame target following before and after full connection：These features are cascaded respectively by selecting the feature in target proximity region after convolutional layer extraction feature and input full articulamentum by present frame and previous frame image.The present invention effectively ensures ship detection and the real-time and stability of tracking.

Description

A kind of sea ship video detection and tracking based on deep learning
Technical field
The invention belongs to sea ship video detections and tracking technique field more particularly to a kind of sea ship video detection With the method for tracking.
Background technology
The sea visible ray or the target detection of infrared image obtained based on imaging sensor is had safely in coast defence extensively should With.Detection zone is limited to offshore or out of, certain marine point visual range, and sea motion carrier and marine marker are equal Good system deployment place can be used as.
Traditional video image sea-surface target detection method mainly has the Weak Classifier cascade side based on boosting frames Method and the target matching method based on support vector machines.But the feature representation ability of these methods is limited, can not cope with Sea Surface Ship It is illumination variation under the conditions of the variation of shape is various, ship occurs in video the variation of angular dimension, different weather, red Outer image imaging effect poor with respect to visible ray etc..Therefore it is serious to the missing inspection of target and flase drop situation.
On the other hand, monitor video usually requires that high real-time and fine definition, however, these two aspects is shifting Relationship.The resolution ratio of high clear video image be 1920*1080, such input picture, traditional target based on sliding window Detection method will generate million grades of couple candidate detection window, take seriously, can not accomplish in real time.
Invention content
It is an object of the invention to solve above-mentioned technical problem, a kind of sea ship video inspection based on deep learning is provided Survey and tracking, the ship in video image is detected using the method based on deep learning convolutional neural networks with Track simultaneously carries out high parallel computation using GPU, to ensure real-time and stability.
Technical scheme is as follows：
A kind of sea ship video detection and tracking based on deep learning, carry out according to the following steps：
(1) quickly convolution model feature extraction：Clarification of objective is extracted by trunk convolutional network；Using depth convolution Neural network is learned and takes target ship feature automatically, each characteristic layer, all goes extraction feature using a series of 3x3 convolution kernels, Wherein, the convolutional layer of shallow-layer mainly extracts minutia, and the convolutional layer of deep layer mainly extracts abstract global contour feature, and rolls up Following sample level connection between lamination；
(2) multiple dimensioned full convolution model target positioning and classification：The big measure feature obtained using trunk convolutional network, more Scale convolutional layer is reintegrated, and constructs the target signature pyramid network that scale successively decreases, and each layer represents different size of mesh Target feature carries out fusion comparison between these characteristic layers, to determine the size and location of target；
(3) frame target following before and after full connection：After present frame and previous frame image extract feature by convolutional layer respectively, The feature in target proximity region is selected, these features are cascaded and inputs full articulamentum.
Further, video frame is normalized to the input for being converted into fixed size as Feature Selection Model, soon The convolution Feature Selection Model of speed uses 18 layers of convolutional neural networks structure, by multiple continuous convolutional layer interval ponds layer group Into.
Further, the multiple continuous convolutional layer interval pond layer composition structure is：Convolutional layer * 2- ponds layer-volume Lamination * 2- ponds layer-convolutional layer * 3- ponds layer-convolutional layer * 3- ponds layer-convolutional layer * 3- ponds layer.
Further, convolutional layer uses the convolution kernel of 3*3, with 1 convolution last layer input data of step-length, then plus one B is biased, linear transformation is converted into nonlinear transformation by correcting linear unit R eLU activation primitives,, wherein, which n represents Layer network, X are characteristics of image, and W is convolution kernel weight, and g (x) is activation primitive.
Further, the pond layer uses 2*2 convolution, and with 2 convolution last layer input data of step-length, sample mode is most Input feature vector figure is mapped to a characteristic pattern for reducing 4 times by big value sampling, each pond layer.
Further, pyramidal each layer of the feature multiple dimensioned full convolution model obtained is set close as output layer Identical candidate frame is spent, the candidate frame on pyramid top corresponds to target frame larger in original image, the candidate frame pair at pyramid bottom Should in original image smaller target frame.
Further, Multi-task loss calculating is carried out to the candidate frame；Each ROI outputs discrete probability distribution For：It is with the bounding box displacements returned, wherein, k represents the index of classification, and parameter x, y refers to relative to candidate target The translational movement of frame Scale invariant, parameter w, h refer in log space relative to the Gao Yukuan of candidate target frame；Loss function represents For：, wherein, it is true classification, is Classification Loss, is positioning loss, is the candidate frame to match with ground truth box Number, be weight；Classification Loss is calculated using softmax functions, the real value DUAL PROBLEMS OF VECTOR MAPPINGs that a k is tieed up are into one The constant vector of range 0-1 is classified according to its size；
Further, in known previous frame object space, region of the present frame near the position where object is original It scans for, in t-1 frames, it is assumed that target position is (cx, cy), and size is (w, h), then extracting a block size is The image block of (2w, 2h) is input in CNN；In t frames, equally also centered on (cx, cy), extraction size is (2w, 2h) Image block, be input in CNN；What full articulamentum learnt is a complicated feature comparison function, exports the opposite of target Movement, the then output of full articulamentum are connected to the layer of 4 nodes, represent respectively bounding-box top left co-ordinates with Target length and width, so as to export the position of target.
Beneficial effects of the present invention：Realize imaging sensor to sea ship from motion tracking, detecting and tracking algorithm Accuracy and robustness are all critically important, for this purpose, the present invention uses the method based on deep learning convolutional neural networks to video figure Ship as in is detected and tracks, while carries out high parallel computation using GPU, effectively ensures ship detection and tracking Real-time and stability.
Specific embodiment
Embodiments of the present invention are illustrated by particular specific embodiment below, those skilled in the art can be by this explanation Content disclosed by book understands other advantages and effect of the present invention easily.
With reference to embodiment, the invention will be further described：
The flow of sea ship video detection and tracking based on deep learning is as follows：
1st, quick convolution model feature extraction：
Clarification of objective is extracted by trunk convolutional network.The spy that traditional target detection tracking method passes through engineer Sign modeling target, such as LBP, HOG, ICF, the ability to express of feature are limited.This technology invention is using depth convolutional neural networks Automatic learn takes target ship feature, has merged the characteristic information on multiple abstraction hierarchies.Each characteristic layer all employs a system The 3x3 convolution kernels of row go extraction feature, and the convolutional layer of shallow-layer mainly extracts minutia, and the convolutional layer of deep layer mainly extracts abstract Global contour feature.Following sample level connection, makes characteristic layer scale gradually reduce, forms feature pyramid between convolutional layer. Video frame is normalized to the input for being converted into fixed size as Feature Selection Model.Quick convolution feature extraction mould Type uses 18 layers of convolutional neural networks structure, is made of multiple continuous convolutional layer interval ponds layer.Appropriate network depth Guaranteeing to extract the features of enough complexity, calculating speed is quick enough simultaneously.Concrete structure is as follows：Convolutional layer * 2- ponds layer-volume Lamination * 2- ponds layer-convolutional layer * 3- ponds layer-convolutional layer * 3- ponds layer-convolutional layer * 3- ponds layer.
Convolutional layer uses the convolution kernel of 3*3, with 1 convolution last layer input data of step-length, then plus a biasing b, leads to It crosses and corrects linear unit R eLU activation primitives linear transformation is converted into nonlinear transformation.Xn=g (WTXn-1+bn), g (x)= ReLU (x)=max (0, x) (which layer network n represents, and X is characteristics of image, and W is convolution kernel weight, and g (x) is activation primitive). For linear function, the ability to express of ReLU is stronger, is especially embodied in depth network, and for nonlinear function and Since the gradient in non-negative section is constant gradient disappearance problem is not present so that the convergence rate of model maintains in speech, ReLU In a stable state.
Pond layer uses 2*2 convolution, with 2 convolution last layer input data of step-length.Sample mode is sampled for maximum value.Each Input feature vector figure is mapped to a characteristic pattern for reducing 4 times by pond layer.
2nd, multiple dimensioned full convolution model target positioning and classification：
The big measure feature obtained using trunk convolutional network, is reintegrated in multiple dimensioned convolutional layer, constructs scale and successively decrease Target signature pyramid network, each layer represents different size of clarification of objective, merged between these characteristic layers Comparison, the size and location of final determining target.
Pyramidal each layer of the feature that multiple dimensioned full convolution model is obtained is as output layer, the identical time of setting density Frame is selected, the candidate frame on pyramid top corresponds to target frame larger in original image, and the candidate frame at pyramid bottom corresponds to original image In smaller target frame.Multi-task loss calculating is carried out to these candidate frames.Discrete probability distribution is exported to each ROI: P=(p0,...,pk) and bounding box return displacementWherein k represents the index of classification, the first two Parameter x, y refer to the translation relative to candidate target frame Scale invariant, latter two parameter w, and h refers in log space relative to time Select the Gao Yukuan of target frame.Loss function is expressed as：Wherein k*It is true Real classification, LconfIt is Classification Loss, LlocIt is positioning loss, N is with the ground truth box candidate frames to match Number, α is weight.Classification Loss is calculated using softmax functions, the real value DUAL PROBLEMS OF VECTOR MAPPINGs of k dimension into a model The constant vector of 0-1 is enclosed, is classified according to its size.
Work as | x |<When 1, smoothL1(x) equal to 0.5x2, when x takes its residual value, smoothL1(x) it is equal to | x | -0.5.
3rd, frame target following before and after full connection：
After present frame and previous frame image extract feature by convolutional layer respectively, the spy in target proximity region is selected Sign, these features are cascaded and input full articulamentum, the purpose of full articulamentum is to compare the target that previous frame detects The feature of feature and present frame, with find target be moved to where.Known previous frame object space, present frame is in object original The region near position where coming scans for.In t-1 frames, it is assumed that target position is (cx, cy), and size is (w, h) then extracts the image block that a block size is (2w, 2h) and is input in CNN.In t frames, also centered on (cx, cy), The image block that size is (2w, 2h) is extracted, is input in CNN.What full articulamentum learnt is that a complicated feature compares letter Number, exports the relative motion of target.Then the output of full articulamentum is connected to the layer of 4 nodes, represents respectively Bounding-box top left co-ordinates and target length and width, to export the position of target.
The above-described embodiments merely illustrate the principles and effects of the present invention, and is not intended to limit the present invention.It is any ripe The personage for knowing this technology all can carry out modifications and changes under the spirit and scope without prejudice to the present invention to above-described embodiment.Cause This, those of ordinary skill in the art is complete without departing from disclosed spirit and institute under technological thought such as Into all equivalent modifications or change, should by the present invention claim be covered.

Claims (8)

1. a kind of sea ship video detection and tracking based on deep learning, which is characterized in that carry out according to the following steps：
(1) quickly convolution model feature extraction：Clarification of objective is extracted by trunk convolutional network；Using depth convolutional Neural Network is learned and takes target ship feature automatically, each characteristic layer, all goes extraction feature using a series of 3x3 convolution kernels, wherein, The convolutional layer of shallow-layer mainly extracts minutia, and the convolutional layer of deep layer mainly extracts abstract global contour feature, and convolutional layer Between the connection of following sample level；
(2) multiple dimensioned full convolution model target positioning and classification：The big measure feature obtained using trunk convolutional network, multiple dimensioned Convolutional layer is reintegrated, and constructs the target signature pyramid network that scale successively decreases, and each layer represents different size of target Feature carries out fusion comparison between these characteristic layers, to determine the size and location of target；
(3) frame target following before and after full connection：After present frame and previous frame image extract feature by convolutional layer respectively, choose Go out the feature in target proximity region, these features are cascaded and input full articulamentum.
2. sea ship video detection and tracking based on deep learning according to claim 1, which is characterized in that will The input for being converted into fixed size as Feature Selection Model, quick convolution Feature Selection Model is normalized in video frame Using 18 layers of convolutional neural networks structure, it is made of multiple continuous convolutional layer interval ponds layer.
3. sea ship video detection and tracking based on deep learning according to claim 2, which is characterized in that institute Stating multiple continuous convolutional layer interval ponds layer composition structure is：Convolutional layer * 2- ponds layer-convolutional layer * 2- ponds layer-convolution Layer * 3- ponds layer-convolutional layer * 3- ponds layer-convolutional layer * 3- ponds layer.
4. sea ship video detection and tracking based on deep learning according to claim 3, which is characterized in that volume Lamination uses the convolution kernel of 3*3, with 1 convolution last layer input data of step-length, then plus a biasing b, by correcting linearly Linear transformation is converted to nonlinear transformation, X by unit R eLU activation primitivesn=g (WTXn-1+bn), g (x)=ReLU (x)=max (0, x), wherein, which layer network n represents, and X is characteristics of image, and W is convolution kernel weight, and g (x) is activation primitive.
5. sea ship video detection and tracking based on deep learning according to claim 4, which is characterized in that institute Pond layer is stated using 2*2 convolution, with 2 convolution last layer input data of step-length, sample mode is sampled for maximum value, each pond layer Input feature vector figure is mapped to a characteristic pattern for reducing 4 times.
6. sea ship video detection and tracking based on deep learning according to any one of claim 1-5, special Sign is that pyramidal each layer of the feature for obtaining multiple dimensioned full convolution model is as output layer, the identical time of setting density Frame is selected, the candidate frame on pyramid top corresponds to target frame larger in original image, and the candidate frame at pyramid bottom corresponds to original image In smaller target frame.
7. sea ship video detection and tracking based on deep learning according to claim 6, which is characterized in that right The candidate frame carries out Multi-taskloss calculating, and each ROI outputs discrete probability distribution is：P=(p0,...,pk) and Boundingbox return displacement beWherein, k represents the index of classification, and parameter x, y refers to relative to time The translational movement of target frame Scale invariant is selected, parameter w, h refers in log space relative to the Gao Yukuan of candidate target frame；Lose letter Number is expressed as：Wherein, k*It is true classification, LconfIt is Classification Loss, LlocIt is positioning loss, N is the number of the candidate frame to match with groundtruthbox, and α is weight；Using softmax functions Calculate Classification Loss, the realvalue DUAL PROBLEMS OF VECTOR MAPPINGs of k dimension into the constant vector of a range 0-1, according to its size come Classify；
8. the sea ship video detection and tracking, feature according to claim 1 or 7 based on deep learning exist In in known previous frame object space, region of the present frame near the position where object is original scans for, in t- In 1 frame, it is assumed that target position is (cx, cy), and size is (w, h), then extracts the image block that a block size is (2w, 2h) It is input in CNN；In t frames, equally also centered on (cx, cy), extraction size is the image block of (2w, 2h), is input to In CNN；What full articulamentum learnt is a complicated feature comparison function, exports the relative motion of target, then full connection The output of layer is connected to the layer of 4 nodes, represents bounding-box top left co-ordinates and target length and width respectively, so as to defeated Go out the position of target.
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CN110675377A (en) * 2019-09-20 2020-01-10 国网湖北省电力有限公司电力科学研究院 State monitoring system and method for substation relay protection device
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CN114820716A (en) * 2022-06-29 2022-07-29 中兴软件技术(南昌)有限公司 Target tracking method and system based on computer vision
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