Identification and performance of an action related to a poorly parked vehicle

    公开(公告)号:US11176824B2

    公开(公告)日:2021-11-16

    申请号:US16224052

    申请日:2018-12-18

    Abstract: A device can receive parking metadata that includes location data indicating that a portion of a vehicle is located outside of a designated parking area (DPA). The device can process the parking metadata to identify values that are to be used when determining actions to perform. The device can obtain supplemental events data associated with events occurring near the DPA. The device can determine the actions to perform based on the parking metadata and the supplemental events data. The device can provide, as one of the actions and to one or more other devices or to the vehicle, a message indicating that the portion of the vehicle is located outside of the DPA. This can cause the one or more other devices or the vehicle to: move the vehicle from the DPA, reposition the vehicle within the DPA, or penalize an owner of the vehicle.

    SMART CONVEYOR BELT SERVICE
    2.
    发明公开

    公开(公告)号:US20230347384A1

    公开(公告)日:2023-11-02

    申请号:US17732108

    申请日:2022-04-28

    Abstract: A smart conveyor belt web service is provided. A network device generates recipes for multiple orders. Each recipe provides sorting instructions for a conveyor system. The network device transfers an identifier for a first recipe to a queue of active recipes and sends the first recipe to a server device for the conveyor system. The network device receives a recipe-complete status message indicating completion of the first recipe. The network device deletes the indicator for the first recipe from the queue based on receiving the recipe-complete status message and transfers a second recipe to the queue of active recipes to initiate processing of another order.

    Machine learning-based device placement and configuration service

    公开(公告)号:US10715714B2

    公开(公告)日:2020-07-14

    申请号:US16162482

    申请日:2018-10-17

    Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based device placement and configuration service is provided. The machine learning-based device placement and configuration system uses regression models calculated from installation and performance data. The regression model includes data associated with sites at which video cameras were previously installed and tested for detection accuracy levels possibly associated with a service area. With these generated models, information about the physical space, and desired performance criteria, designers optimize camera number, camera placement, and geo-location camera parameters. The device placement and configuration service may find an optimal camera placement and geo-location camera parameter set which satisfy certain criteria. The geo-location parameters include position, height, heading, pitch, and roll. Subsequent to the installation of the video cameras using the calculated geo-location parameters, the system may verify accuracy of detection of the service area, and update the regression model.

    MACHINE LEARNING-BASED DEVICE PLACEMENT AND CONFIGURATION SERVICE

    公开(公告)号:US20200304703A1

    公开(公告)日:2020-09-24

    申请号:US16896366

    申请日:2020-06-09

    Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based device placement and configuration service is provided. The machine learning-based device placement and configuration system uses regression models calculated from installation and performance data. The regression model includes data associated with sites at which video cameras were previously installed and tested for detection accuracy levels possibly associated with a service area. With these generated models, information about the physical space, and desired performance criteria, designers optimize camera number, camera placement, and geo-location camera parameters. The device placement and configuration service may find an optimal camera placement and geo-location camera parameter set which satisfy certain criteria. The geo-location parameters include position, height, heading, pitch, and roll. Subsequent to the installation of the video cameras using the calculated geo-location parameters, the system may verify accuracy of detection of the service area, and update the regression model.

    Machine learning-based device placement and configuration service

    公开(公告)号:US10939031B2

    公开(公告)日:2021-03-02

    申请号:US16896366

    申请日:2020-06-09

    Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based device placement and configuration service is provided. The machine learning-based device placement and configuration system uses regression models calculated from installation and performance data. The regression model includes data associated with sites at which video cameras were previously installed and tested for detection accuracy levels possibly associated with a service area. With these generated models, information about the physical space, and desired performance criteria, designers optimize camera number, camera placement, and geo-location camera parameters. The device placement and configuration service may find an optimal camera placement and geo-location camera parameter set which satisfy certain criteria. The geo-location parameters include position, height, heading, pitch, and roll. Subsequent to the installation of the video cameras using the calculated geo-location parameters, the system may verify accuracy of detection of the service area, and update the regression model.

    Monitoring a scene to analyze an event using a plurality of image streams

    公开(公告)号:US10885775B2

    公开(公告)日:2021-01-05

    申请号:US16433615

    申请日:2019-06-06

    Abstract: A simulation platform may receive, from a plurality of image capture devices, a plurality of image streams that depict an event. The simulation platform may identify an object that is depicted in each of the plurality of image streams. The simulation platform may determine, for each of the plurality of image streams, respective image-based coordinates of a path associated with the object during the event. The simulation platform may determine, based on the respective image-based coordinates and timestamps of the plurality of image streams, simulation coordinates associated with a path of the object during the event. The simulation platform may detect, based on the simulation coordinates, that the object is involved in a collision during the event. The simulation platform may perform an action associated with detecting that the object is involved in the collision.

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