Adaptive Motion Filtering in an Unmanned Autonomous Vehicle

    公开(公告)号:US20190182428A1

    公开(公告)日:2019-06-13

    申请号:US16324308

    申请日:2017-02-24

    Abstract: Embodiments include devices and methods for adaptive image processing in an unmanned autonomous vehicle (UAV). In various embodiments, an image sensor may capture an image, while a processor of the UAV obtains attitude information from one or more attitude sensors. Such information may include the relative attitude of the UAV and changes in attitude. The processor of the UAV may determine a UAV motion mode based, at least in part, on the obtained attitude information. The UAV motion mode may result in the modification of yaw correction parameters. The processor of the UAV may further execute yaw filtering on the image based, at least in part, on the determined motion mode.

    Adaptive Image Processing in an Unmanned Autonomous Vehicle

    公开(公告)号:US20190174063A1

    公开(公告)日:2019-06-06

    申请号:US16324351

    申请日:2016-09-23

    Abstract: Embodiments include devices and methods for adaptive image processing in an umnanned autonomous vehicle (UAV). In various embodiments, an image sensor may capture an image. Images may be obtained during motion or hover modes of the UAV. The UAV may determine whether stabilizing a line of the image causes a breach of an image crop margin. That is, the UAV may estimate or begin to adjust image distortion and crop the image, and may evaluate during or after the estimation/adjustment whether an image crop margin is breached by the result. The UAV may reduce stabilizing of the line of the image in response to determining that stabilizing the line of the image causes a breach of the image crop margin.

    VEHICLE ENTRY DETECTION
    3.
    发明申请

    公开(公告)号:US20220067479A1

    公开(公告)日:2022-03-03

    申请号:US17274602

    申请日:2019-10-08

    Abstract: Certain aspects of the present disclosure are generally directed to apparatus and techniques for event state detection. One example method generally includes receiving a plurality of sensor signals at a computing device, determining, at the computing device, probabilities of sub-event states based on the plurality of sensor signals using an artificial neural network for each of a plurality of time intervals, and detecting, at the computing device, the event state based on the probabilities of the sub-event states via a state sequence model.

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