64 results about "Re identification" patented technology

The present disclosure is directed to methods and systems for data-driven de-identification tool that can detect personally identifiable information (PII) elements within a given dataset and apply selective mapping transformations rules to de-identify the dataset. The disclosed data de-identification tool identifies direct identifiers, quasi-identifiers and unique values within a structured dataset in an example embodiment. The data de-identification tool then transforms these potentially personal or sensitive data elements into de-identified data elements and replaces the identified direct identifiers, quasi-identifiers and unique values within the structured dataset with the de-identified data elements. The data de-identification tool calculates a risk of re-identification and based on the risk level, repeat the de-identification process iteratively until the risk levels are within an acceptable range.

A system and method that allows privacy-enhanced querying to occur, where re-identification risk is reduced to a user-configured level are described. The system and method include a query federation agent that analyses and augments user-submitted queries to include results that contain metadata relating to the privacy characteristics. The system and method ensure results that contain privacy risks above defined thresholds are suppressed or altered so that re-identification cannot occur. The system and method add noise to results that contain privacy risks above defined thresholds so that re-identification cannot occur. The system and method utilize a data profile that defines the sources of potential re-identification risk in a database schema. The system and method apply privacy rules that are configurable for different database tables and configurable thresholds for different types of privacy risks.

A method for generating an optimised domain-generalisable model for re-identification of a target in a set of candidate images. The method optimises a local feature embedding model for domain-specific feature representation at each client of a plurality of clients, then receives, at a central server, information on changes to the local feature embedding model at each respective client resulting from the optimising step, and then updates a global feature embedding model based on the changes to the local feature embedding model. The method further receives, at each client from the central server, information representative of the updates to the global feature embedding model, then maps, at each client, on to the respective local feature embedding model at least a portion of the received updates, and subsequently updates, at each client, the respective local feature embedding model based on the mapped updates. The steps are repeated until convergence criteria are met, wherein the global feature embedding model is the optimised domain-generalisable model for re-identification of a target in a set of candidate images.

The invention discloses an ancient building construction drawing automatic verification method based on machine learning, and the method comprises the following steps: carrying out the preprocessing of a construction drawing image, and extracting clear boundary information; secondly, extracting geometric structure features of the primitives through an image analysis technology, such as line segments, curves and closed regions; secondly, generating a classification template by utilizing historical drawing data, and establishing an identification model by combining geometric features and semantic information of the primitives; the model can intelligently classify and accurately identify each component in a drawing, and judges whether the structure is matched with the semantics or not. When a suspected abnormal area is found, the system automatically recognizes and marks the suspected abnormal area again, a set of complete drawing verification process is formed, and the method has universality suitable for various ancient building style drawings. The invention aims to reduce the time and error rate of manual drawing auditing.

Systems and methods for tracking and re-identifying tracked subjects in an area of real space are disclosed. The method includes generating first and second reidentification feature vectors of a first subject identified from a first time interval from the respective first and second sequences of images as obtained from the first time interval. The method includes generating third and fourth reidentification feature vectors of a second subject identified from a second time interval from the respective first and second sequences of images. The method includes matching a second subject identified from a second time interval with the first subject identified from the first time interval when at least one of a first similarity score between the first and the third reidentification feature vectors and a second similarity score between the second and the fourth reidentification feature vectors is above a pre-defined threshold.

The method delivers a degree of protection (txP3) representative of the risk of re-identification of data in the case of a correspondence search attack including a deterministic search based on an external information source and a correspondence search based on a distance. The method comprises steps of E) consolidating a set of original individuals (EDO) and a set of anonymous individuals (IA); F) identifying, in the set of original individuals, individuals at risk (IOrs) via the deterministic correspondence search; G) evaluating a degree of failure of re-identification (txP1) for the sets of original individuals and of anonymous individuals, on the basis of the correspondence search based on distance; H) computing the degree of protection as a function of a total number of individuals in the original dataset, of a number (RS) of individuals at risk identified in step B) and of the degree of failure of re-identification (txP1).

A data anonymization pipeline system for managing holding and pooling data is disclosed. The data anonymization pipeline system transforms personal data at a source and then stores the transformed data in a safe environment. Furthermore, a re-identification risk assessment is performed before providing access to a user to fetch the de-identified data for secondary purposes.

The invention discloses a vehicle multi-target tracking method capable of keeping a unique mark number, and belongs to the field of computer vision, aiming at the problem that the target mark number is not unique after the observation angle is changed or shielded during multi-target tracking. Vehicle videos blocked at the same observation angle or at different observation angles are collected to construct a data set, and the data set is processed according to the following process: inputting a video 1, tracking to obtain a vehicle target label and position, and intercepting a target picture frame by frame to form the data set for training a DenseNet121 network; inputting a video 2, tracking to obtain target information, inputting a target picture into the trained DenseNet121 network for re-identification, reserving an old label for an old target, endowing a new label for a new target, intercepting a new target picture to expand a data set, and updating the training network by using the data sets of the video 1 and the video 2; and inputting a video 3 and a subsequent video, and repeating the steps, namely tracking the target, re-identifying the label, expanding the data set and updating network training.

This invention relates to a panoramic target search and monocular linkage tracking method, apparatus, and computer device. The method includes acquiring target information; identifying the target in a panoramic image using a target detection algorithm; determining the target monocular camera corresponding to the target from the panoramic image; tracking the target using the target monocular camera according to a preset method, the preset method including cyclically switching between a monocular tracking mode of a first preset duration and a panoramic shooting mode of a second preset duration; when the target monocular camera switches to the panoramic shooting mode, re-synthesizing an updated panoramic image; determining whether the target has been lost from the field of view of the target monocular camera in monocular tracking mode; if so, re-identifying the target from the updated panoramic image. The linkage mechanism between monocular and panoramic tracking in this invention allows for flexible adjustment of the tracking strategy, maintaining tracking accuracy and stability, and enhancing tracking robustness in complex scenes.

The invention relates to a method and system for re-identification. The method includes: processing first original image data by applying a first image processing setting; detecting a first object; extracting a first feature vector; processing the second original image data by applying a second image processing setting; detecting a second object; extracting a second feature vector; determining the similarity of the first object and the second object by comparing the first feature vector with the second feature vector; if the difference between the first feature vector and the second feature vector exceeds a first threshold and the difference between the first image processing setting and the second image processing setting exceeds a second threshold, reprocessing the first original image data corresponding to the first image region by applying the second image processing setting; extracting an updated first feature vector from the first image region; the updated first feature vector is compared to the second feature vector. The disclosure further relates to an image processing system configured to perform the method.

The present disclosure is directed to methods and systems for data-driven de-identification tool that can detect personally identifiable information (PII) elements within a given dataset and apply selective mapping transformations rules to de-identify the dataset. The disclosed data de-identification tool identifies direct identifiers, quasi-identifiers and unique values within a structured dataset in an example embodiment. The data de-identification tool then transforms these potentially personal or sensitive data elements into de-identified data elements and replaces the identified direct identifiers, quasi-identifiers and unique values within the structured dataset with the de-identified data elements. The data de-identification tool calculates a risk of re-identification and based on the risk level, repeat the de-identification process iteratively until the risk levels are within an acceptable range.

A method of object re-identification is disclosed, in particular a method of object re-identification in images of objects. The method comprises providing a plurality of neural networks (27) for object re-identification, wherein each of the plurality of neural networks is trained on image data of a different group of anatomical features, each group being represented by a reference vector; receiving a plurality of images (4) of an object (38) and an input vector representing anatomical features depicted in all of the plurality of images (4); comparing the input vector to the reference vectors to determine a most similar reference vector according to a predefined condition; and inputting image data of the plurality of objects (38) to the neural network (#1) represented by the most similar reference vector to determine whether the plurality of objects (38) have the same identity.

The present application relates to the field of personnel re-tracking, and particularly relates to a personnel re-tracking method based on image detection technology. The method first acquires a monitoring image of a monitoring area in real time, performs human body detection on a current frame image, screens out a tracking target in the current frame image, performs skeleton point detection on the tracking target in the current frame image, and judges whether the tracking target is a human body target according to the number of skeleton points, generates a distance relationship table containing the distance between skeleton points of the human body target for the current frame image, judges whether the human body target is lost according to the number of images containing the human body target, and re-identifies and tracks the human body target in a new monitoring image according to the difference between a new distance relationship table and a historical distance relationship table. The present application can solve the problems of high target false detection rate in the existing personnel tracking algorithm, low re-identification accuracy after tracking loss and weak anti-interference ability.

Controlling a list of inactive trajectories for re-identification in an object tracking system is disclosed. The present disclosure relates to methods (600), systems, and software for controlling a list of inactive trajectories in an object tracking system. The described techniques include using a decision threshold to obtain (S604) a false alarm rate (FPR) of a re-identification model and obtain (S606) an acceptable FPR of a tracking system. The re-identification model is used to match the new detection with the inactive trajectory. The technique includes determining (S608) one or more termination conditions for deleting the inactive trajectory based on the acceptable FPR and the model FPR. If the first inactivity trajectory satisfies a termination condition, the first inactivity is deleted from the list (S610).

A method, system and software for controlling a list of inactive tracks in an object tracking system. The techniques described includes obtaining the false positive rate, FPR, of a re-identification model using a decision threshold, and obtaining an acceptable FPR of the tracking system. The re-identification model is used to match new detections with inactive tracks. The techniques include determining one or more terminal conditions for deleting inactive tracks based on the acceptable and model FPRs. If a first inactive track meets a terminal condition, it is deleted from the list.

In an embodiment an apparatus includes a processor configured to generate a feature extraction module using a dataset in which an attribute for each object is defined, receive an image obtained by a camera, extract an attribute of an object of interest from the image using the learned feature extraction module, identify an object re-identification candidate group based on the extracted attribute of the object of interest and re-identify the object of interest based on the identified object re-identification candidate group.

A system for monitoring safety compliance comprises a plurality of cameras and a computing system. The plurality of cameras are configured to obtain data, and the data comprises multiple images associated with one or more objects. The computing system is configured to process the data to determine the safety compliance of the one or more objects associated with the multiple images based on implementing a first model trained for re-identification. The computing system is further configured to train the first model for re-identification, by determining a similarity loss and updating the first model based on the similarity loss.

A computer-implemented method of tracking objects in a video sequence of a scene comprises determining a location of a sink in the scene where objects exit the scene and a location of a source where objects enter the scene; tracking a first object moving in the scene using a re-identification algorithm, wherein the first object is associated with a re-identification threshold of the re-identification algorithm; detecting that the first object has exited the scene at the sink; and responsive to detecting that the first object has exited the scene at the sink, adjusting the re-identification threshold associated with the first object such that a probability that the re-identification algorithm re-identifies a second object, entering the scene at the source after the first object has exited the scene at the sink, as the first object is reduced.

A method for generating an optimised domain-generalisable model for re-identification of a target in a set of candidate images. The method optimises a local feature embedding model for domain-specific feature representation at each client of a plurality of clients, then receives, at a central server, information on changes to the local feature embedding model at each respective client resulting from the optimising step, and then updates a global feature embedding model based on the changes to the local feature embedding model. The method further receives, at each client from the central server, information representative of the updates to the global feature embedding model, then maps, at each client, on to the respective local feature embedding model at least a portion of the received updates, and subsequently updates, at each client, the respective local feature embedding model based on the mapped updates. The steps are repeated until convergence criteria are met, wherein the global feature embedding model is the optimised domain-generalisable model for re-identification of a target in a set of candidate images.

An example apparatus for re-identifying objects includes an image receiver to receive a first image and a second image of an object with an identity. The apparatus also includes a fused model generator to fuse a global representation of the object with local representations of pose parts of the object to generate a fused representation of the object based on the first image. The apparatus further includes an object re-identifier to re-identify the object with the identity in the second image based on the fused representation.

According to the human re-identification method according to the present disclosure, first, estimating a pose of a human to be re-identified in an image of the human is executed. Next, clipping a predetermined number of patches from the image along a body of the human based on the pose is executed. Then, generating positional information of each of the predetermined number of patches is executed. Then, inputting the predetermined number of patches with the positional information into a vision transformer encoder is executed. Then, inputting an output of the vision transformer encoder into a neural network is executed. And finally, obtaining an output of the neural network as a re-identification result of the human is executed.

A system and method for distributed data intersection using privacy preserving collaborative detection is disclosed The system includes a processor and a memory that causes the processor to verify data sets and configurations from a first and second entity interacting in a distributed data intersection to ensure data integrity and privacy during a data collaboration. The processor enables computations between the first and second entity using cryptographic protocols to identify common data points. The processor allows the first and second entity to retrieve the common data points pertaining to other entity using a oblivious data sharing technique while maintaining confidentiality of the data accessed during the interaction. The processor facilitates validation of data integrity and accuracy without revealing underlying information. The processor controls data-sharing policies ensuring that authorized data is disclosed based on a predefined criteria thereby enabling collaboration of data for re-identification while protecting privacy.

A system for providing data privacy risk of data privacy related data objects for assigning an access right for processing and storing the data objects, including receiving more than one data object and extracting data from the data objects, generating at least one identity space, wherein the identity space includes those data objects which include extracted data elements concerning a group of individuals or a subset of the group of individuals, and wherein the group of individuals differs from the group of individuals of each other identity space, calculating at least one privacy risk for each identity space and for the contained individuals depending on re-identification probabilities of each individual based on the extracted data elements in the identity space and depending on a set of background knowledge which is assumed to be known, exporting the extracted data and export the calculated privacy risk.

An example apparatus for re-identifying objects includes an image receiver to receive a first image and a second image of an object with an identity. The apparatus also includes a fused model generator to fuse a global representation of the object with local representations of pose parts of the object to generate a fused representation of the object based on the first image. The apparatus further includes an object re-identifier to re-identify the object with the identity in the second image based on the fused representation.

The invention relates to the technical field of data processing, in particular to an industrial Internet of Things electromagnetic security event rapid identification and processing method, which comprises the following steps: according to the electromagnetic signal quality and the operation environment of industrial Internet of Things equipment in each operation time period in which no electromagnetic security event occurs, obtaining environment interference intensity in different operation environments; acquiring a signal parameter used for identifying an electromagnetic safety event, and acquiring an initial threshold interval of the signal parameter according to the operation state and the environmental interference intensity of the industrial Internet of Things equipment in each operation period; re-identifying whether the industrial Internet of Things equipment has an electromagnetic safety event in each historical time period by using the initial threshold interval to obtain a false alarm rate and a missing alarm rate; according to the method, the initial threshold interval is corrected according to the false alarm rate and the missing alarm rate, the final threshold interval of the signal parameters is obtained, the electromagnetic safety event is recognized in real time according to the final threshold interval of the signal parameters, and the recognition accuracy of the electromagnetic safety event is improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

A data anonymization pipeline system for managing holding and pooling data is disclosed. The data anonymization pipeline system transforms personal data at a source and then stores the transformed data in a safe environment. Furthermore, a re-identification risk assessment is performed before providing access to a user to fetch the de-identified data for secondary purposes.

The invention discloses an unmanned aerial vehicle target tracking system under an incomplete information condition, and the system comprises a multi-source data collection module which synchronously obtains data in a flight environment of an unmanned aerial vehicle through a sensing assembly carried by an unmanned aerial vehicle platform; the multi-source data preprocessing module is used for executing different preprocessing modes for different data types, and generating a standardized feature data set through standardization and alignment; the multi-source data fusion and prediction module takes the standardized feature data set as input, realizes multi-information dynamic fusion of a sensing component through an improved Kalman filtering algorithm, and outputs optimal state estimation of a tracking target; the shielding prediction module is used for judging whether the data are shielded or not based on the data quality, and if yes, lightweight updating is triggered; and the feature adaptive updating module comprises an online learning updating unit and a re-identification optimization unit and is used for updating the target feature model in real time. According to the invention, the tracking stability and accuracy of the unmanned aerial vehicle in a complex environment are improved.

A tracking method according to an example of the present disclosure may include generating, by a generation device, a first feature based on a first frame through a backbone, generating, by the generation device, first detection information indicating a detection result for a first object based on the first feature through a first neck for object detection, generating, by the generation device, a first feature vector for a visual feature of the first object based on the first feature through a second neck for object re-identification, and / or performing, by a tracking device, tracking based on the first detection information and the first feature vector.