PROCESSING METHOD, DEVICE AND STORAGE MEDIUM FOR REAR VIEW IMAGE

    公开(公告)号:US20240037957A1

    公开(公告)日:2024-02-01

    申请号:US18352227

    申请日:2023-07-13

    CPC classification number: G06V20/56 G06T7/11 G06T7/64 G06F3/14 G06V2201/07

    Abstract: The present disclosure provides a processing method, a device and a storage medium for rear view image. The processing method includes following steps: obtaining an original rear view image; obtaining vehicle data to determine at least one basic curvature; obtaining driver information to determine at least one floating curvature; and processing the rear view image according to the at least one basic curvature and the at least one floating curvature. By performing these steps, the processing method can adjust display curvature of the rear view image according to specific driving scenario and user needs, thereby clearly and personalized displaying rear view image with suitable range and display scale on the display screen with limited area, to help the user clearly observe situations on the left, right and rear sides of the vehicle.

    VIDEO ANALYSIS-BASED SELF-CHECKOUT APPARATUS FOR PREVENTING PRODUCT LOSS AND ITS CONTROL METHOD

    公开(公告)号:US20240029441A1

    公开(公告)日:2024-01-25

    申请号:US18349992

    申请日:2023-07-11

    CPC classification number: G06V20/52 G06V20/60 G06V40/20 G06V20/44 G06V2201/07

    Abstract: The present disclosure relates to a self-checkout apparatus. The self-checkout apparatus comprises: a product recognition table which is provided with a product identification zone and on which a product to be identified is located; a first camera which is arranged so that a photographing direction is toward the product identification zone and which obtains a video of the product in the product identification zone by photographing the product identification zone; a second camera which obtains a video of a surveillance area by photographing the surveillance area including the product recognition table and the inside of a store; and a product identifier which detects an identification code assigned to the product from the video of the product obtained by the first camera and interprets the detected identification code to output an identification result of the product captured by the first camera.

    HUMAN-ASSISTED NEURO-SYMBOLIC OBJECT AND EVENT MONITORING

    公开(公告)号:US20240029422A1

    公开(公告)日:2024-01-25

    申请号:US17871335

    申请日:2022-07-22

    Abstract: A human-assisted neuro-symbolic system for outputting fine-grained classifications and corresponding images or video of a desired object or scene. The system includes one or more cameras configured to generate a video feed of a scene. One or more processors are programmed to generate video analytics data from the video feed, including coarse-grained classification data regarding one or more objects in the scene. A knowledge graph is built with instantiated (e.g., time-based) domain ontology of the one or more objects in the scene. The domain ontology can be augmented via human-in-the-loop. Once augmented, the knowledge graph can be infused into a deep learning model, such as a natural language model. An input (e.g., in natural language) can seek fine-grained input characteristics, and the deep learning model infused with the knowledge graph retrieves a corresponding portion of the video feed with the fine-grained input characteristics.

    Determining region attribute
    386.
    发明授权

    公开(公告)号:US11877716B2

    公开(公告)日:2024-01-23

    申请号:US16762448

    申请日:2018-10-31

    Abstract: Methods, apparatus and systems for determining a region attribute, and electronic devices are provided. In one aspect, a method includes: identifying a marker line in a target map, the target map being a map of a to-be-cleaned target scene, an intelligent cleaning device relying on the target map during a cleaning process, determining an enclosed region and an unenclosed region with a first position as a reference point in the target map, based on the identified marker line and an auxiliary object in the target map, the auxiliary object including a map boundary and an obstacle, the first position being a position of a preset reference object in the target map, determining the enclosed region as a user-defined cleaning region for the cleaning process of the intelligent cleaning device, and determining the unenclosed region as a normal cleaning region for the cleaning process of the intelligent cleaning device.

    SYSTEM, APPARATUS, AND METHOD FOR MONITORING EDGE COMPUTE SITES

    公开(公告)号:US20240013545A1

    公开(公告)日:2024-01-11

    申请号:US18372831

    申请日:2023-09-26

    Applicant: RF Code, Inc.

    Abstract: Systems, methods, and devices are described for monitoring and protecting electronic hardware, data assets, and the facility itself, particularly well-suited to monitoring remote facilities and resources in edge locations, including for determining door state for both equipment and personnel doors, and for determining values of one or more environmental parameters, using optical image analytics. An example system includes one or more edge-located hardware monitoring devices having one or more physical sensors, and custom software distributed across edge, cloud, mobile, and enterprise platforms. An example monitoring device can include an embedded computer, various sensors of different types, one or more cameras, a power supply, and several communication interfaces. All or virtually all sensing and processing capabilities can be integrated into the edge device, which utilizes low-cost sensors and video analytics to monitor the environment.

    OBJECT DETECTION SYSTEM AND OBJECT DETECTION ASSISTANT SYSTEM

    公开(公告)号:US20240013430A1

    公开(公告)日:2024-01-11

    申请号:US18308057

    申请日:2023-04-27

    Abstract: An object detection assistant system includes a memory and a processor. The processor is coupled to the memory. The memory stores one or more commands. The processor accesses and executes one or more commands of the memory. One or more commands include inputting a detection result parameter output by an object detection neural network for object detection of an image to an assistant neural network to output a first correction coefficient after processing by the assistant neural network, where the detection result parameter includes object information and a first confidence; inputting the first correction coefficient and detection result parameters to a Bayesian classifier to output a second correction coefficient; and adjusting the first confidence according to the second correction coefficient to obtain second confidence, and the second confidence being taken as the first confidence of the adjusted detection result parameter.

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