20 results about "Re identification" patented technology

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.

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.

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.

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.

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 a program based on a data anonymization system wherein the system comprises a module for identifying data and assigning an exposure level with respect to individuals inside or outside the user's organization; a module for identifying feared events and their severity; a module for assessing a legal anonymization criteria avoiding re-identification of individuals; a module for evaluating the level of exploitability of the data; a module for assessing the overall risk of the dataset, in which data with a significant level of exploitability and / or severity are identified; and a module for correcting data and implementing countermeasures or transforming data with a significant level of exploitability and / or severity, in order to reduce that level.

The present application relates to the technical field of data processing, and more particularly to an image restoration method based on image detection and region extraction, comprising S1, establishing a Chinese character pattern library; S2, an AI processing module acquires the binary grayscale coordinates of a target image and identifies the content in the target image; S3, the AI processing module performs correlation determination on the Chinese character content in the acquired target image to confirm the rationality of the Chinese character content in the target image; S4, the AI processing module outputs a new electronic document according to the content encoding in the target image; and S5, the AI processing module performs deep learning on the comparison process and comparison result. The present application can make the paper version file directly output a clear digital signal version document after scanning and archiving, and improve the processing efficiency, by converting the paper version file into an electronic digital signal output, and then re-identifying, correcting, supplementing and re-entering the Chinese character content.

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 method for updating a neural network model for object re-identification includes: storing a neural network model pre-trained for object re-identification; acquiring images from a surveillance camera device; detecting objects from the images and, obtaining training data from among the objects to update the neural network model according to a predetermined criterion; and inputting the training data to the neural network model in a feedforward manner to obtain image characteristic parameters corresponding to the training data, and updating the neural network model by reflecting the image characteristic parameters in the neural network model.

A method and system for cctv-integrated monitoring is disclosed.The present disclosure relates to a system and a method for CCTV-integrated monitoring, which find and provide a current location and a movement route of a monitored subject to be monitored through a similarity comparison considering a re-identification difficulty between a human area image acquired from a real-time CCTV video after escaping, and a secured monitored subject image.

A computer-implemented method (100) for performing description mining of a concept is provided. The method includes: acquiring (S110) a plurality of reference descriptions, each of which is associated with a user interface (UI) element corresponding to a type of UI element represented by the concept; generating (S120) at least one of a local description and a global description of the concept based on the plurality of reference descriptions, wherein the local description of the concept includes a description component specific to the corresponding UI, and wherein the global description of the concept includes a description component not specific to the UI; and storing (S130) the generated local and global descriptions such that they are associated with a concept identifier corresponding to the concept.

The present invention relates to a method and system for tracking multiple objects based on deep learning by considering directional information of objects, which: generate a region of interest in an image captured on a road; derive an intersection angle on the basis of the region of interest; and when there is a re-identified vehicle object while tracking a plurality of vehicle objects on the road, correct location coordinates of the corresponding vehicle object according to the region of interest and the intersection angle and continue tracking.

Disclosed are systems for de-identifying individual data to reduce the chances of re-identification of individuals when training machine learning models. A system can include one or more processors that are configured to obtain individual data for an individual; obtain sample data generated based on an output of a processing system at a second point in time when processing a sample obtained from the individual; and generate a treatment profile based on the individual data and the sample data. The one or more processors can be configured to de-identify the treatment profile of the individual to generate a limited treatment profile. In examples, the one or more processors can then be configured to provide the limited profile data associated with the limited treatment profile to a device to allow the device to execute one or more operations involved in training or implementing a neural network.

A system for monitoring compliance of a worker wearing personal protective equipment includes a plurality of cameras and a computing system. The plurality of cameras are configured to obtain data, and the data includes a plurality of images associated with one or more objects. The computing system is configured to process the data to determine safety compliance of the one or more objects associated with the plurality of images based on an implementation of a first model trained for re-identification. The computing system is further configured to update the first model trained for re-identification based on a similarity loss.

The invention discloses a photovoltaic module crack identification optimization method based on edge calculation, and the method comprises the following steps: S1, collecting a module image and electrical characteristics, building an input data set, and storing module layout information; s2, deploying a crack identification model, performing morphological graph transformation to extract features, and performing sparse ridge line decoding; s3, calculating a power loss estimated value and obtaining an actual measurement result in combination with the electrical parameters; s4, comparing an estimated value with a measured value, generating a physical consistency score, and dynamically correcting a confidence coefficient and a threshold value to form an identification result; s5, when the identification result is low in confidence or divergence, active sampling is triggered, polarization re-shooting is executed, and identification is carried out again; and S6, generating a weak sample packet according to an uncertain result, uploading the weak sample packet to the cloud, issuing an increment weight after re-checking, and outputting a final recognition and health report. According to the method, morphological graph modeling, physical consistency correction and an active sampling mechanism are introduced, so that high-precision, real-time and verifiable identification of the cracks of the photovoltaic module is realized.

The present application provides methods of tracking objects. According to an aspect, a computer-implemented method of tracking objects in a video sequence of a scene is provided, the method comprising: determining a location in the scene at which an object exits a sink of the scene and a location at which the object enters a source of 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 in response to detecting that the first object has exited the scene at the sink, adjusting the re-identification threshold associated with the first object to reduce a probability that the re-identification algorithm will re-identify a second object as the first object, the second object entering the scene at the source after the first object has exited the scene at the sink.