Abstract: A registered object image storage unit stores at least one image of an object as a registered object image in association with an object identification number that uniquely identifies the object. The unknown object image acquisition unit acquires an unknown object image that is an image of an unknown object. An object re-identification unit re-identifies the acquired unknown object image by using the registered object image stored in the registered object image storage unit. When a condition that all registered object images stored in the registered object image storage unit in association with the object identification number of the object in the unknown object image whose re-identification succeeded differ from the unknown object image, a registered object image update unit adds the unknown object image to the registered object image storage unit as a new registered object image in association with the object identification number.

Abstract: An image processing apparatus includes: an image accruing means acquiring a plurality of images captured at different times; a personal item detecting means detecting a personal item from each of the images; a skeleton information detecting means detecting skeleton information of a person from each of the images; a reidentification personal item selecting means selecting a personal item to reidentify by the detected personal item and skeleton information of the person; and a personal item reidentifying means determining whether or not a personal item to reidentify selected from one image of the plurality of images and a personal item to reidentify selected from another image of the images are identical personal items.

Abstract: A video processing system includes an image quality control apparatus and a detection apparatus. The image quality control apparatus includes an image quality control unit configured to control image quality of each region of a video, and a transmission unit configured to transmit, to the detection apparatus, the video of which the image quality is controlled. The detection apparatus includes a detection unit configured to detect information regarding an object in the video transmitted from transmission unit, and a notification unit configured to notify the image quality control apparatus of a detection result of the detection unit. The image quality control apparatus further includes a determination unit configured to determine the image quality of each region of the video according to the detection result notified from the notification unit, the image quality being controlled by the image quality control unit.

Abstract: A video processing system (10) includes an object detection unit (11) that detects an object included in a video input to the video processing system (10) in a case where the video is input to the video processing system (10). The video processing system (10) further includes a video quality control unit (12) that controls a video quality of a region including the object in the input video according to a situation related to the object detected from the input video in a case where the object detection unit (11) detects the object from the input video.

Abstract: A video processing apparatus according to one aspect of the present example embodiment includes: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: generate image quality feature information indicating a feature of image quality information indicating an image quality of a video in time and space; generate integrated data obtained by integrating information regarding a video including a feature of the video in time and space and the image quality feature information; and execute recognition processing on a subject included in the video based on the integrated data.

Abstract: A person retrieval system includes a person/carrying object image extraction unit that extracts, from an input image, a person/carrying object image that is an image of a person to be retrieved including an image of a carrying object, a carrying object image extraction unit that extracts an image of the carrying object from the person/carrying object image, a person image calculation unit that calculates a person image that is the person/carrying object image after processing is made on the carrying object part in the person/carrying object image so as to prevent a significant feature amount from being extracted, a person feature amount extraction unit that extracts a person feature amount from the person image, and a determination unit that compares the person feature amount with a registered person feature amount of a registered person, and determines whether the person to be retrieved is the same as the registered person.

Abstract: A video processing system (10) according to the present disclosure includes a recognition model (M1), a recognition model (M2), a recognition model (M3), and a recognition model (M4) that have learned video learning data corresponding to different video quality parameters, for each of the video quality parameters; and a selection unit (11) that selects a recognition model that performs recognition regarding a target included in the video input data to be input, from among the recognition model (M1), the recognition model (M2), the recognition model (M3), and the recognition model (M4), according to a video quality parameter of the video input data.

Abstract: Provided is a video processing system including: a detection means for detecting a tracking target from an input video; a trajectory prediction means for predicting a trajectory of the tracking target in the video; and a tracking means for tracking the tracking target, and when there is a missing region in the video, for estimating a position of the tracking target in the missing region using a prediction result of the trajectory prediction means. The video processing system may further include a determination means for determining whether or not the tracking target tracked by the tracking means is the same as the tracking target detected by the detection means; and a trajectory information output means for assigning identification information to the detected tracking target.

Abstract: An object is to provide a video processing system, a video processing apparatus, and a video processing method that can be expected to improve recognition accuracy of an object in a video. A video processing system includes video acquisition means, time difference information acquisition mean, and recognition means. The video acquisition means acquires an input video. The time difference information acquisition means acquires first time difference information between the frames of the input video. The recognition means inputs the input video and the first time difference information between the frames of the input video to a trained recognition model trained using a training video and second time difference information between frames of the training video and recognizing an object in the input video.

Abstract: A video processing system (10) includes a recognition model (M1) that analyzes a video corresponding to a first video recognition environment; a recognition model (M2) that analyzes a video corresponding to a second video recognition environment; and a switching unit (11) that switches a recognition model for analyzing video input data to be input, from the recognition model (M1) to the recognition model (M2) according to a change from the first video recognition environment to the second video recognition environment in the video input data, in which the switching unit (11) inputs video input data including data preceding a switching timing by a predetermined period to the recognition model 10 (M2) according to the change from the first video recognition environment to the second video recognition environment in the video input data.

Abstract: A video processing system (10) includes an image quality control unit (11) that controls an image quality of a gaze region including a gaze target in an input video, a recognition unit (12) that performs recognition processing of recognizing the gaze target on the video in which the image quality of the gaze region is controlled, a prediction unit (13) that predicts a position of the gaze target in a video subsequent to the video on which the recognition processing has been performed, based on extraction information extracted from the recognition processing, and a determination unit (14) that determines the gaze region for which the image quality control means (11) controls an image quality in the subsequent video, based on the predicted position of the gaze target.

Abstract: A distributed storage space management method, a computing device and a storage medium are provided. An embodiment is applied to a hardware smart card, which is deployed on a local host as a local hardware smart card. A corresponding storage space is partitioned for a local virtual device from a pre-configured storage device; the partitioned storage space is simulated to generate a simulated storage device corresponding to the local virtual device, for use by the local virtual device. A storage space application request of a remote hardware smart card is received, and according to the request, a corresponding storage space is partitioned for a remote virtual device from the pre-configured storage device, so that the partitioned storage space is simulated by means of the remote hardware smart card to generate a simulated storage device corresponding to the remote virtual device, for use by the remote virtual device.

Abstract: Embodiments of the present application provide a data access method and system, a hardware offloading device, an electronic device, and a medium. In the embodiments of the present application, on one hand, a data access task for an object-based storage system is transferred from an electronic device to a hardware offloading device for execution, which can reduce processing pressure on the electronic device, improve processing performance of the electronic device, and enhance data access performance. On the other hand, after every time receiving a to-be-written file from an application, the hardware offloading device does not directly write the to-be-written file into the object-based storage system, but first caches it locally, and after a plurality of to-be-written files are cached, the hardware offloading device merges the plurality of to-be-written files into a new object file to write it into the object-based storage system.

Abstract: Embodiments of the present specification provide a virtual resource processing method and apparatus, where the virtual resource processing method includes: acquiring a data processing request received by a network card driver module within a preset time period; determining, according to a data processing type corresponding to the data processing request, a first resource consumption result corresponding to a virtual storage module of a target virtual machine and a second resource consumption result corresponding to a virtual network module of the target virtual machine; and adjusting, according to the first resource consumption result and/or the second resource consumption result, virtual resources of the virtual storage module and the virtual network module.

Abstract: A system and a method for managing a namespace of an offloading card and processing an input/output request. The system for managing the namespace of the offloading card (300) includes a host (301) and an offloading card (302) connected to the host (301), where the host (301) has multiple applications issuing an input/output request running thereon, and the host (301) sends a namespace creation request to the offloading card (302), the offloading card (302) creates corresponding multiple namespaces for the multiple applications according to the namespace creation request, the offloading card (302) allocates multiple hardware queues corresponding to the created multiple namespaces according to the namespace creation request and binds the allocated multiple hardware queues respectively to the corresponding namespaces; multiple namespaces can be created, and dedicated hardware queues can be dynamically allocated for respective namespaces, so that namespace resources in hardware level is achieved.

Abstract: In a learning apparatus, an acquisition unit acquires image data and label data corresponding to the image data. An object candidate extraction unit extracts each object candidate rectangle from the image data. A correct answer data generation unit generates a background object label corresponding to each background object included in each object candidate rectangle as correct answer data corresponding to the object candidate rectangle by using the label data. A prediction unit predicts a classification using each object candidate rectangle and outputs a prediction result. An optimization unit optimizes the object candidate extraction unit and the prediction unit using the prediction result and the correct answer data.

Abstract: A learning apparatus includes an environment information acquisition unit and a learning unit. The environment information acquisition unit acquires environment information concerning a learning image. The learning unit detects an object detection model that detects each target object included in the learning image by using the environment information.

Abstract: An information processing apparatus (2000) detects one or more candidate regions (22) from a captured image (20) based on an image feature of a target object. Each candidate region (22) is an image region that is estimated to represent the target object. The information processing apparatus (2000) detects a person region (26) from the captured image (20) and detects an estimation position (24) based on the detected person region (26). The person region (26) is a region that is estimated to represent a person. The estimation position (24) is a position in the captured image (20) where the target object is estimated to be present. Then, the information processing apparatus (2000) determines an object region (30), which is an image region representing the target object, based on each candidate region (22) and the estimation position (24).

Abstract: An information processing apparatus (2000) detects one or more candidate regions (22) from a captured image (20) based on an image feature of a target object. Each candidate region (22) is an image region that is estimated to represent the target object. The information processing apparatus (2000) detects a person region (26) from the captured image (20) and detects an estimation position (24) based on the detected person region (26). The person region (26) is a region that is estimated to represent a person. The estimation position (24) is a position in the captured image (20) where the target object is estimated to be present. Then, the information processing apparatus (2000) determines an object region (30), which is an image region representing the target object, based on each candidate region (22) and the estimation position (24).

Abstract: An image processing system includes a training unit that generates a trained model performing a plurality of mutually different inference tasks from an image. The trained model includes: a first component that extracts a first feature value common to the plurality of inference tasks from the image; a second component that is provided for each of the inference tasks and extracts a second feature value specific to the corresponding inference task from the first feature value; a third component that generates a third feature value by concatenating the second feature values extracted for the respective inference tasks; and a fourth component that is provided for each of the inference tasks and outputs an inference result of the corresponding inference task from the third feature value.