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34results about How to "Improve re-identification performance" patented technology
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Pedestrian re-identification method based on depth multi-loss fusion model
PendingCN110008842AReduce environmental differencesImprove re-identification performanceImage enhancementGeometric image transformationData setFeature extraction
The invention relates to a pedestrian re-identification method based on a depth multi-loss fusion model. According to the method, a deep learning technology is used, pre-processing operations such asoverturning, cutting, random erasing and style migration are carried out on the training set picture. Feature extraction is carried out through a basic network model. A plurality of loss functions areused for carrying out fusion joint training on the network. Compared with a pedestrian re-identification algorithm based on deep learning, since the method uses a plurality of preprocessing modes, fusion of three loss functions and an effective training strategy, the pedestrian re-identification performance on the data set is greatly improved. On one hand, multiple preprocessing modes can expanda data set, the generalization capability of the model is improved, the occurrence of an overfitting condition is avoided, and on the other hand, the three loss functions have respective advantages and disadvantages, and when the three loss functions are effectively combined, the used model can obtain a better recognition result.
Owner:TONGJI UNIV +2
Pedestrian re-identifying method based on coordination scale learning
ActiveCN103793702AMeet hard-to-get requirementsImprove re-identification performanceCharacter and pattern recognitionPositive sampleVideo retrieval
The invention discloses a pedestrian re-identifying method based on coordination scale learning and belongs to the technical field of monitoring video retrieval. First, according to color and texture features of images in a marked training sample set L, scale learning is carried out, and covariance matrixes Mc and Mt in corresponding Mahalanobis distance are obtained; and checking targets are selected randomly, the Mc and the Mt are used for Mahalanobis distance measuring, a corresponding sorting result is obtained, positive samples and negative samples are obtained, a new marked training sample set L is obtained, the Mc and the Mt are updated until an unmarked training sample set U is empty, a final marked sample set L* is obtained, the color and texture features are fused, an Mf is obtained, and a Mahalanobis distance function based on the Mf can be used for pedestrian re-identifying. Under a semi-supervised framework, the pedestrian re-identifying technology based on scale learning is studied, scale learning is carried out with the marked samples assisted by the unmarked samples, the requirement that practical video investigation application marked training samples are hard to obtain is met, and re-identifying performance under few marked samples can be effectively improved.
Owner:WUHAN UNIV
Multi-scale pedestrian re-identification method based on multi-granularity depth feature fusion
PendingCN112818931AImprove re-identification performanceRelieve stressCharacter and pattern recognitionNeural architecturesComputer visionMachine learning
The invention discloses a multi-scale pedestrian re-identification method based on multi-granularity depth feature fusion, and the method comprises the steps: selecting a pedestrian re-identification data set, and carrying out the preprocessing of a training set in the data set; selecting a residual network as a basic skeleton, including a global coarse-grained fusion learning branch, a local coarse-grained fusion learning branch and a local attention fine-grained fusion learning branch; adopting softmax loss and triple loss as re-identification network supervisors, and training a pedestrian re-identification network model; and fusing the network features of different branches as a final descriptor of the pedestrian, and taking the to-be-queried pedestrian image as the input of the pedestrian re-identification network model to obtain a pedestrian re-identification result. According to the method, the pressure of a complex background or posture change on a re-identification task is effectively relieved, and the identification precision is improved.
Owner:CHINA UNIV OF MINING & TECH
A pedestrian re-identification method based on deep multi-view feature distance learning
ActiveCN109902590AImprove robustnessIncrease the proportionCharacter and pattern recognitionFeature vectorFeature extraction
The pedestrian re-identification method based on deep multi-view feature distance learning is specifically implemented according to the following steps: step 1, extracting region feature vectors; Step2, region division: according to all the feature vectors of the image obtained in the step 1, carrying out normalization through a normalization algorithm l2 norm; Representing a vector set of the image in a summation mode, and performing l2 norm normalization processing on image representation; dividing One image into N regions, and obtaining depth region aggregation characteristics; Step 3, LOMO feature extraction: respectively extracting traditional LOMO features from pedestrian images in the reference set and the test set; 4, carrying out multi-view feature distance learning, and obtaining two distances from the two aspects of depth region aggregation features and LOMO features through XQDA training of the two features; Step 5, a weighted fusion strategy: carrying out parameter weighted fusion on the two distances obtained in the step 4 to obtain a final distance, and obtaining a matched grade according to the final distance; The robustness of pedestrian re-identification can be obviously improved; And the pedestrian re-identification performance is improved.
Owner:青岛类认知人工智能有限公司
Double-angle sequencing optimization based pedestrian re-identification method
ActiveCN104200206AReliable search resultsImprove re-identification performanceCharacter and pattern recognitionVideo retrievalAlgorithm
The invention discloses a double-angle sequencing optimization based pedestrian re-identification method, and belongs to the technical field of surveillance video retrieval. The double-angle sequencing optimization based pedestrian re-identification method comprises the steps of: firstly obtaining initial sequencing results through ways based on global characteristics and local characteristics; then taking an intersection set GKc(p) of front k results of the two sequencing results as initial values for inquiring a more reliable nearest neighbor; then regarding each goal gKc(j) in GKc(p) as a new inquiring factor for crossed opposite inquiry, weighting and fusing a sequence list of the opposite inquiry to obtain a whole optimized sequence list; finally, establishing a double-layer map for each gKc(j) in the GKc(p), and calculating neighbor similarity of the map to perform further rearrangement on the GKc(p) so as to further obtain a better re-identification effect.
Owner:WUHAN UNIV
Pedestrian re-identification method based on video appearance and motion information synchronous enhancement
ActiveCN111259786AImprove re-identification performanceImprove the extraction effectBiometric pattern recognitionNeural architecturesFeature extractionVideo prediction
The invention discloses a pedestrian re-identification method based on video appearance and motion information synchronous enhancement. During training, pedestrian appearance and motion information ina backbone network are respectively enhanced through an appearance enhancement module AEM and a motion enhancement module MEM. The appearance enhancement module AEM uses an attribute recognition model obtained by training an existing large-scale pedestrian attribute data set to provide an attribute pseudo label for the large-scale pedestrian video data set, and enhances appearance and semantic information through attribute learning; the motion enhancement module MEM predicts pedestrian gait information by using a video prediction model, enhances gait information features with identity discrimination ability in a pedestrian feature extraction backbone network, and improves pedestrian re-identification performance. In practical application, only the pedestrian feature extraction backbone network needs to be reserved, and higher pedestrian re-identification performance can be obtained without increasing the network complexity and the model size. And the enhanced backbone network featuresobtain higher accuracy in a video-based pedestrian re-identification task.
Owner:ZHEJIANG UNIV
Pedestrian re-recognition method based on similarity and dissimilarity fusion ranking optimization
ActiveCN104462550AReliable search resultsSort results are reliableBiometric pattern recognitionSpecial data processing applicationsPositive sampleRanking
The invention discloses a pedestrian re-recognition method based on similarity and dissimilarity fusion ranking optimization. Pedestrian re-recognition is a specific pedestrian retrieval problem. The main ideal of the pedestrian re-recognition method based on the similarity and dissimilarity fusion ranking optimization is that correctly-retrieved pedestrian targets are similar to polar positive samples of queried pedestrians and are not similar to polar negative samples of the queried pedestrians, meanwhile the pedestrians similar to the polar positive samples are defined as suspected positive samples, the samples similar to the polar negative samples defined as suspected negative samples, and the pedestrian re-recognition improving effect is achieved by improving ranking of suspected positive samples and meanwhile reducing ranking of suspected negative samples in a ranking optimization mode. In addition, the similarity and dissimilarity relation is strengthened by fusing results of multiple methods. By means of the pedestrian re-recognition method based on the similarity and dissimilarity fusion ranking optimization, similarity and dissimilarity ranking results are fused to further improve the matching accuracy of the same row of pedestrians under multiple cameras.
Owner:WUHAN UNIV
Pedestrian re-identification method based on triple convolutional neural network
ActiveCN109635695AImprove re-identification performanceImprove the performance of joint optimizationCharacter and pattern recognitionNeural architecturesNerve networkRe identification
The invention discloses a pedestrian re-identification method based on a triple convolutional neural network. The method comprises the steps of 1, forming a small batch of samples; 2, outputting global features and local features of each pedestrian image; 3, calculating the distance between the global features of every two pedestrian images in the small-batch samples and the distance between the local features of the corresponding body parts; 4, forming P x K pairs of triple samples; 5, using a Log-logistic function to replace a hinge function to improve a triple convolutional neural network loss function; 6, obtaining a trained triple convolutional neural network; asnd step 7, identifying the target pedestrian image by using the trained triple convolutional neural network, and further avoiding mismatching between the target pedestrian image and the pedestrian image to be identified by combining a confidence measurement mechanism. According to the pedestrian re-identification method based on the triple convolutional neural network, pedestrian re-identification can be accurately carried out, and mismatching between different pedestrians is avoided.
Owner:XIAN UNIV OF TECH
Vehicle re-identification method based on multi-loss fusion model
ActiveCN111382690AImprove re-identification performanceImprove generalization abilityInternal combustion piston enginesCharacter and pattern recognitionEngineeringData mining
The invention discloses a vehicle re-identification method based on a multi-loss fusion model. According to the method, a deep convolutional neural network structure is designed for the problem of vehicle re-identification, a multi-loss fusion model is adopted to jointly supervise the training of the deep convolutional neural network, the joint optimization of the same ID sample difference and different ID sample differences of vehicles is realized, and the purpose of learning feature expression with higher discriminability is achieved. Wherein the proposed multi-cluster center loss function can increase the inter-class distance and shorten the intra-class distance, so that the vehicle features belonging to the same ID are close to the class center as much as possible, and the discrimination of feature expression is effectively improved. According to the multi-loss fusion model provided by the invention, multiple data enhancement modes are combined, so that the vehicle re-identification precision is effectively improved.
Owner:NANJING UNIV OF SCI & TECH
Dynamic low-resolution pedestrian re-identification method based on scale distance gradient function interface learning
InactiveCN106599795AEffective re-identificationImprove re-identification performanceCharacter and pattern recognitionRe identificationImage resolution
The invention discloses a dynamic low-resolution pedestrian re-identification method based on scale distance gradient function interface learning, and is to obtain a good re-identification effect when images are low in resolution and change in scale. The method comprises the following steps: to begin with, obtaining an image pair, that is, a positive sample pair, from the same person, and image pairs, that is, negative sample pairs, from different persons; then, introducing distance scale gradient functions, wherein the positive sample pair generates a feasible distance scale gradient function, and the negative sample pair generates an infeasible distance scale gradient function, and the two kinds of distance scale gradient functions form a distance scale gradient function space, a positive parameter vector expressing the feasible distance scale gradient function, and a negative parameter vector expressing the infeasible distance scale gradient function; and finally, carrying out classification through a trained random forest classifier to judge whether the images are from the same object according to the information that whether some distance scale gradient function is in a positive domain or a negative domain in the function space.
Owner:WUHAN UNIV
Pedestrian re-identification method based on multi-layer fusion and alignment division
PendingCN111881780AImprove re-identification performanceViolent division method optimizationCharacter and pattern recognitionNeural architecturesPattern recognitionEngineering
The invention discloses a pedestrian re-identification method based on multi-layer fusion and alignment division, and the method comprises the following steps: constructing a pedestrian re-identification network model, and training the pedestrian re-identification network model; fusing the feature maps of different layers with the feature map of the last layer by using a multi-layer fusion modulein the network model to obtain a multi-layer fusion feature finally containing shallow feature information; extracting the central position of the pedestrian by using an alignment division module in the network model, and expanding the central position to two sides to obtain the local features of the pedestrian accurately segmenting the local area; and connecting the multi-layer fusion features, the local features and the global features according to channel dimensions to obtain final pedestrian discrimination features to complete pedestrian re-identification. The invention has the beneficialeffects that the proposed fusion module can fuse information carried by feature maps of different levels, and on the basis, multi-layer fusion features are extracted and added into final discrimination features for auxiliary identification, so the re-identification performance is effectively improved.
Owner:上海蠡图信息科技有限公司
Pedestrian re-recognition method based on multi-task learning
InactiveCN112149538AEasy to trainImprove re-identification performanceImage analysisBiometric pattern recognitionTraining data setsTask learning
The invention provides a pedestrian re-recognition method based on multi-task learning. The method comprises the steps: obtaining a cross-camera pedestrian image, and constructing a pedestrian re-recognition training data set; constructing a multi-task learning network, wherein the network can combine an attribute task and an identity recognition task, all parameters and losses can be optimized ina combined mode, and therefore the purpose of improving pedestrian re-recognition accuracy is achieved. The attribute task and the recognition task respectively comprise a verification step, a verification step, a classification step and a verification loss to optimize the distance of the sample, the recognition loss is used for constructing a large class space and verifying the loss at the sametime, and the space is optimized by minimizing similar images and maximizing the distance between different images so as to improve the recognition precision.
Owner:NANJING UNIV OF POSTS & TELECOMM
Semi-supervised character re-identification method based on camera style and human body posture adaptation
ActiveCN111639540ACases that fit the class distributionImprove performanceBiometric pattern recognitionInternal combustion piston enginesHuman bodyData expansion
The invention discloses a semi-supervised character re-identification method based on camera styles and human body posture adaptation. The method comprises the following steps: selecting labeled datacovering all identities and all camera styles; carrying out data expansion on the labeled data to generate images with different postures in the camera and images with different styles between the cameras; training a network model by combining the expanded labeled data with the unlabeled data; and performing pseudo label distribution by a strategy of constraining the average distance between the label-free data and the label-containing data characteristics and the number of each type of images. According to the method, the problems of small label data volume and unreasonable pseudo label distribution in the prior art are solved.
Owner:OCEAN UNIV OF CHINA
Pedestrian re-identification method based on label uncertainty and a human body component model
ActiveCN109829414AImprove performanceGreat application valueCharacter and pattern recognitionIdentification rateRe identification
The invention discloses a pedestrian re-identification method based on label uncertainty and a human body component model. The pedestrian re-identification method comprises the following steps: (1) constructing a deep neural network model based on a human body component; (2) initializing the constructed deep neural network model, and performing training according to the constructed deep neural network structure to obtain another model; (3) training the initialized deep neural network model and updating parameters in the network; (4) carrying out feature extraction on the target pedestrian image and pedestrian images in a pedestrian image library by adopting the trained deep neural network; And (5) performing cosine similarity calculation and sorting on the extracted features to obtain a recognition result. According to the method, the problems that local component information is not completely accurate and classification confidence coefficients of all local components are large in difference are effectively processed, the correct rate of pedestrian re-identification can be effectively improved, and / or the false identification rate can be effectively reduced.
Owner:SOUTH CHINA UNIV OF TECH
Pedestrian re-identification method based on global distance scale loss function
ActiveCN109993070AImprove robustnessImprove generalization abilityBiometric pattern recognitionNeural learning methodsRobustificationCosine similarity
The invention discloses a pedestrian re-identification method based on a global distance scale loss function. The method comprises the following specific steps: carrying out data enhancement processing on training data of a pedestrian re-identification data set; randomly selecting each batch of data; constructing a deep neural network based on human body components and initializing the network; using a cross entropy loss function and a loss function based on a global distance scale to simultaneously supervise training of the deep neural network; and performing feature extraction on the targetpedestrian image and the pedestrian image in the pedestrian image library, performing cosine similarity calculation, and performing sorting to obtain an identification result. According to the loss function of the global distance scale based on the statistical characteristics, noise interference can be effectively avoided, the risk of overfitting can be effectively reduced, and the robustness andthe generalization capability of the model can be improved.
Owner:SOUTH CHINA UNIV OF TECH
Pedestrian image recognition method based on deep network model
ActiveCN111177447AImprove re-identification performanceGood re-identification performanceCharacter and pattern recognitionStill image data clustering/classificationNetwork modelCosine Distance
The invention provides a pedestrian image recognition method based on a deep network model. The pedestrian image recognition method comprises the steps of performing data preprocessing on a pedestrianimage; executing an adaptive sampling algorithm on the preprocessed data to obtain a batch with more difficult samples; extracting multilayer features through a backbone network model; enhancing thelow-level features by using a sub-module, performing downscaling, splicing the low-level features with the high-level features to obtain multi-level features, segmenting the multi-level features according to different granularities to form a multi-branch structure, extracting component features and global features of each branch, and splicing all the extracted features to obtain depth representation of the pedestrian image; training the constructed network model; extracting the depth representation of the query images through the trained network model, and returning the recognition result of each query image according to the cosine distance similarity between each query image and the queried set. Through the multi-level multi-granularity pedestrian re-recognition depth model, the optimal pedestrian re-recognition performance at the present stage is realized.
Owner:NANJING UNIV
A method for generating pedestrian re-identification data under different illumination conditions based on an adversarial network
ActiveCN109726669AImprove recognition performanceLow costCharacter and pattern recognitionGenerative adversarial networkRe identification
The invention discloses a method for generating pedestrian re-identification data under different illumination conditions based on an adversarial network. The method comprises the following steps: firstly, acquiring videos of different time periods in a monitoring video, namely monitoring videos under different illumination conditions; Extracting representative monitoring images from the acquiredmonitoring videos, and marking the representative monitoring images into three different categories of'too dark, normal and too bright 'according to illumination conditions; Sending the images with the illumination condition labeling information into a generative adversarial network for training, wherein the trained generative network is used for generating data under different illumination conditions; And generating the pedestrian re-identification data into data under different illumination conditions through the trained generation network; And finally, adding the obtained data as a data augmentation form into pedestrian re-identification model training, and at the moment, enabling the model to identify the characteristics of the same target under different illumination conditions, i.e.,enhancing the identification performance of the model on the target under different illumination conditions.
Owner:ZHEJIANG ICARE VISION TECH
Cross-domain pedestrian re-identification method and system based on multi-feature mixed learning
PendingCN113221770AImprove adaptabilityReduce inter-regional variabilityCharacter and pattern recognitionNeural architecturesData setVisual technology
The invention provides a cross-domain pedestrian re-identification method and system based on multi-feature mixed learning, and belongs to the technical field of computer vision. The method comprises: by means of a re-identification model subjected to combined training, extracting pedestrian global features, pedestrian attribute features and pedestrian local features of the pedestrian image to be recognized and a bottom library image, which is similar to the pedestrian identity in the pedestrian image to be recognized, in the image bottom library gallley; and fusing the extracted to-be-identified features, and carrying out similarity matching sorting on the fused features of the features of the bottom library image to obtain a pedestrian re-identification result. According to the method, inter-domain joint training and multi-feature mixed learning are utilized to reduce inter-domain differences, so that the system is more stable and higher in robustness, source domain training of global and local features and joint training of attribute features are performed on images of different scenes, pedestrian attributes are combined, the adaptive capacity of a cross-domain pedestrian re-identification model is improved, and pedestrian re-identification is carried out on a cross-domain data set, so that the cross-domain pedestrian re-identification performance is improved.
Owner:青岛根尖智能科技有限公司
Video-based re-identification method and system for people in smoke scene and terminal
ActiveCN112183338ASolving difficult re-identification problemsGood re-identificationBiometric pattern recognitionNeural architecturesCharacter recognitionNetwork model
The invention belongs to the technical field of character recognition, and discloses a video-based re-identification method and system for people in a smoke scene and a terminal; and the method comprises the steps: constructing a symmetric non-local coding and decoding K estimation network model to carry out defogging of a video; constructing a discrimination network model, and estimating whetherthe input video is a normal video or a fog-free video generated by a defogging sub-network based on the constructed discrimination network model; and constructing a non-local double-attention character re-identification sub-network model, and re-identifying the character. According to the invention, the problem of difficult re-identification caused by foggy re-identification of people in the videocan be solved. According to the invention, people re-identification can be well completed in a foggy video. The whole process of the method is an end-to-end design, and the method can be used more simply. The method is a technology for re-identifying the person in the smoke scene based on the video, end-to-end judgment can be completed, and re-identification of the person can be well completed.
Owner:GUANGDONG UNIV OF PETROCHEMICAL TECH
Pedestrian re-identification method for solving part misalignment
ActiveCN110781817AImprove re-identification performanceGood re-identification performanceImage enhancementImage analysisNetwork modelEngineering
The invention discloses a pedestrian re-identification method for solving part misalignment, and the method comprises the steps: carrying out the data preprocessing of a pedestrian image, adjusting the size of the pedestrian image, carrying out the data enhancement, and carrying out the data standardization processing; constructing a pedestrian re-identification network model, namely constructingdeep representation of a pedestrian image, extracting multilayer features through a backbone network model, enhancing and fusing the multilayer features by using sub-modules to form a multi-branch structure, and extracting component features and global features of each branch; training the constructed network model, defining experiment-related configuration, and optimizing model parameters of thenetwork model; and pedestrian re-identification: extracting the depth representation of the query image through the trained network model, and returning the identification result of each query image according to the similarity between each query image and the queried set after using the second normal form normalization. The pedestrian re-identification method based on fusion of multi-scale features to solve the problem of component misalignment realizes the optimal pedestrian re-identification performance at the present stage.
Owner:NANJING UNIV
Cross-view character recognition method based on shapes and postures under wearable equipment
ActiveCN111582036ACVPI performance improvementImprove re-identification performanceInternal combustion piston enginesCharacter and pattern recognitionHuman bodyTime domain
The invention belongs to the field of computer vision, relates to character recognition, and aims to better achieve character re-recognition with higher accuracy through human body posture information. In order to achieve the purpose, the invention relates to a cross-view character recognition method based on shapes and postures under wearable equipment. The method includes: giving a video frame image of a pedestrian to be detected in the first camera and a video obtained by the second camera; detecting a human body parameterized model and a two-position articulation point position corresponding to the frame image for all the video frames of the first two video frames, wherein the two-dimensional articulation point position is used for optimizing a human body parameterized model Smp1; obtaining a final human body parameterization model through three-dimensional joint point reprojection optimization operation and discrete cosine transform (DCT) time domain optimization operation of thehuman body parameterization model; and comparing the final human body parameterized model of the target to be detected with the human body parameterized model in each video frame in the second camera, and finding out the target to be detected. The method is mainly applied to automatic character recognition occasions.
Owner:TIANJIN UNIV
A method, system, and terminal for re-identifying people in a video-based smog scene
ActiveCN112183338BSolving difficult re-identification problemsGood re-identificationBiometric pattern recognitionNeural architecturesNetwork modelPerson recognition
The invention belongs to the technical field of person recognition, and discloses a method, system and terminal for re-recognition of people in a video-based smog scene, constructing a symmetrical non-local codec K estimation network model to perform defogging processing on the video; constructing a discriminant network model, And based on the built discriminant network model, it is estimated whether the input video is a normal video or a haze-free video generated by the dehazing sub-network; a non-local double-attention person re-identification sub-network model is constructed to re-identify the person. The invention can solve the problem of difficult re-recognition caused by fog in the re-recognition of characters in the video. The present invention can well complete person re-identification in foggy video. The entire process of the present invention is an end-to-end design, which can be used more simply. The invention is a video-based person re-identification technology in a smoky scene, which can complete end-to-end discrimination and can well complete person re-identification.
Owner:GUANGDONG UNIV OF PETROCHEMICAL TECH
A pedestrian re-identification method based on dual-angle ranking optimization
ActiveCN104200206BReliable search resultsImprove re-identification performanceCharacter and pattern recognitionVideo retrievalNear neighbor
Owner:WUHAN UNIV
A method for person re-identification based on video-based synchronous enhancement of appearance and motion information
ActiveCN111259786BImprove re-identification performanceImprove the extraction effectBiometric pattern recognitionNeural architecturesFeature extractionData set
The invention discloses a pedestrian re-identification method based on synchronous enhancement of video appearance and motion information. During training, two modules, an appearance enhancement module AEM and a motion enhancement module MEM, respectively enhance the pedestrian appearance and motion information in a backbone network. The appearance enhancement module AEM uses the attribute recognition model trained by the existing large-scale pedestrian attribute datasets to provide attribute pseudo-labels for large-scale pedestrian video datasets, and enhances appearance and semantic information through attribute learning; the motion enhancement module MEM uses video The prediction model predicts the gait information of pedestrians, enhances the gait information features with identity discrimination in the backbone network of pedestrian feature extraction, and improves the performance of pedestrian re-identification. In practical applications, only the backbone network for pedestrian feature extraction needs to be retained, and higher pedestrian re-identification performance can be obtained without increasing network complexity and model size. The enhanced backbone features achieve higher accuracy in video-based person re-identification tasks.
Owner:ZHEJIANG UNIV
A Pedestrian Re-Identification Method to Solve Misalignment of Components
ActiveCN110781817BImprove re-identification performanceGood re-identification performanceImage enhancementImage analysisModel extractionStructure extraction
Owner:NANJING UNIV
A cross-camera re-identification fusion method and system for objects with similar appearance
ActiveCN109800794BImprove robustnessImprove training efficiencyCharacter and pattern recognitionHierarchical cluster algorithmRadiology
The present invention provides a cross-camera re-identification fusion method for objects with similar appearances, which uses a deep convolutional neural network to extract the global feature map of the picture, and extracts the appearance vector of the target on the global feature map according to the target detection result; encodes the camera, Generate a view vector containing observation view angle information; generate a position vector of the target according to the position of the detection frame corresponding to the target in the image coordinate system. The three vectors are fused and transformed to generate the target representation vector. The network is trained by optimizing the triplet loss function, and the representation vector for re-identification is learned. During the training process, a combination of offline mining and online mining is used to generate and update the triplet dataset. Finally, the hierarchical clustering algorithm with constraints is used to cluster the representation vectors corresponding to the targets in different cameras to realize cross-camera target re-identification. At the same time, a cross-camera re-identification fusion system for objects with similar appearance is provided. The invention improves the accuracy of re-identification.
Owner:SHANGHAI JIAOTONG UNIV
A Pedestrian Re-Identification Method Based on Global Distance Scale Loss Function
ActiveCN109993070BImprove robustnessImprove generalization abilityBiometric pattern recognitionNeural learning methodsCosine similarityData set
The invention discloses a pedestrian re-identification method based on a global distance scale loss function. The specific steps include: performing data enhancement processing on the training data of the pedestrian re-identification data set; randomly selecting each batch of data; constructing a human body component-based Deep neural network and initialize the network; use the cross-entropy loss function and the loss function based on the global distance scale to supervise the training of the deep neural network at the same time; perform feature extraction on the target pedestrian image and pedestrian images in the pedestrian image library, and perform cosine similarity Degree calculation and sorting to get the recognition results. The present invention proposes a loss function of the global distance scale based on statistical characteristics, which can effectively avoid noise interference and reduce the risk of over-fitting, and improve model robustness and generalization ability.
Owner:SOUTH CHINA UNIV OF TECH
Pedestrian re-identification data generation method under different lighting conditions based on adversarial network
ActiveCN109726669BLow costImprove efficiencyCharacter and pattern recognitionPattern recognitionGenerative adversarial network
Owner:浙江捷汇鑫数字科技有限公司
A Pedestrian Re-Identification Method Based on Label Uncertainty and Body Component Model
ActiveCN109829414BAvoid interferenceImprove re-identification performanceCharacter and pattern recognitionHuman bodyCosine similarity
The invention discloses a pedestrian re-identification method based on label uncertainty and a human body component model. The pedestrian re-identification method comprises the following steps: (1) constructing a deep neural network model based on a human body component; (2) initializing the constructed deep neural network model, and performing training according to the constructed deep neural network structure to obtain another model; (3) training the initialized deep neural network model and updating parameters in the network; (4) carrying out feature extraction on the target pedestrian image and pedestrian images in a pedestrian image library by adopting the trained deep neural network; And (5) performing cosine similarity calculation and sorting on the extracted features to obtain a recognition result. According to the method, the problems that local component information is not completely accurate and classification confidence coefficients of all local components are large in difference are effectively processed, the correct rate of pedestrian re-identification can be effectively improved, and / or the false identification rate can be effectively reduced.
Owner:SOUTH CHINA UNIV OF TECH
Pedestrian Re-Identification Method Based on Collaborative Scale Learning
ActiveCN103793702BMeet hard-to-get requirementsImprove re-identification performanceCharacter and pattern recognitionVideo retrievalPositive sample
The invention discloses a pedestrian re-identifying method based on coordination scale learning and belongs to the technical field of monitoring video retrieval. First, according to color and texture features of images in a marked training sample set L, scale learning is carried out, and covariance matrixes Mc and Mt in corresponding Mahalanobis distance are obtained; and checking targets are selected randomly, the Mc and the Mt are used for Mahalanobis distance measuring, a corresponding sorting result is obtained, positive samples and negative samples are obtained, a new marked training sample set L is obtained, the Mc and the Mt are updated until an unmarked training sample set U is empty, a final marked sample set L* is obtained, the color and texture features are fused, an Mf is obtained, and a Mahalanobis distance function based on the Mf can be used for pedestrian re-identifying. Under a semi-supervised framework, the pedestrian re-identifying technology based on scale learning is studied, scale learning is carried out with the marked samples assisted by the unmarked samples, the requirement that practical video investigation application marked training samples are hard to obtain is met, and re-identifying performance under few marked samples can be effectively improved.
Owner:WUHAN UNIV
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