OBJECT COUNT USING MONOCULAR THREE-DIMENSIONAL (3D) PERCEPTION

    公开(公告)号:US20240281990A1

    公开(公告)日:2024-08-22

    申请号:US18172972

    申请日:2023-02-22

    CPC classification number: G06T7/55 G06T2207/10028 G06T2207/30242

    Abstract: Systems and techniques are provided for performing an accurate object count using monocular three-dimensional (3D) perception. In some examples, a computing device can generate a reference depth map based on a reference frame depicting a volume of interest. The computing device can generate a current depth map based on a current frame depicting the volume of interest and one or more objects. The computing device can compare the current depth map to the reference depth map to determine a respective change in depth for each of the one or more objects. The computing device can further compare the respective change in depth for each object to a threshold. The computing device can determine whether each object is located within the volume of interest based on comparing the respective change in depth for each object to the threshold.

    Tracker-Based Security Solutions For Camera Systems

    公开(公告)号:US20250095373A1

    公开(公告)日:2025-03-20

    申请号:US18470924

    申请日:2023-09-20

    Abstract: Various embodiments include methods for identifying inconsistencies in images that could be due to malicious attacks. Various embodiments may include receiving a plurality of camera images from one or more cameras of an apparatus (e.g., a vehicle), performing a plurality of different processes on the plurality of images to detect different types of image inconsistencies, using results of the plurality of different processes on the plurality of images to recognize a vision attack and performing one or more mitigation actions in response to recognizing a vision attack. The plurality of different processes may include temporal consistency checks on the plurality of images spanning a period of time, inconsistency counter checks on the plurality of images that determine whether a number of inconsistencies in camera images satisfies a threshold, past history checks on the plurality of images comparing objects previously recognized to objects recognized in currently obtained images.

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