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公开(公告)号：US20230069197A1
公开(公告)日：2023-03-02
申请号：US17983208
申请日：2022-11-08
Inventor： Wenhao WU , Yuxiang Zhao
Abstract: A method and an apparatus for training a video recognition model are provided. The method may include: dividing a sample video into a plurality of sample video segments; sampling a part of sample video frames from a sample video segment; inputting the part of sample video frames into a feature extraction network to obtain feature information of the sample video segment; performing convolution fusion on the feature information by using a dynamic segment fusion module to obtain fusion feature information, where a convolution kernel of the dynamic segment fusion module varies with different video inputs; inputting the fusion feature information to a fully connected layer to obtain an estimated category of the sample video; and performing a parameter adjustment based on a difference between the tag of a true category and the estimated category to obtain the video recognition model.
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公开(公告)号：US20230036864A1
公开(公告)日：2023-02-02
申请号：US17963058
申请日：2022-10-10
Inventor： Wenhao WU , Yuxiang Zhao
IPC: G06V20/54 , G08G1/052 , G08G1/056 , G08G1/16 , G06V10/762
Abstract: The present disclosure provides a method and apparatus for detecting a traffic anomaly, relates to the field of artificial intelligence and specifically to computer vision and deep learning technologies, and can be applied to video analysis scenarios. A specific implementation comprises: acquiring at least two frames of consecutive traffic images; identifying respectively a position of a target vehicle from the at least two frames of consecutive traffic images to obtain a position information set; determining a direction of travel and speed of the target vehicle according to the position information set; and comparing the direction of travel and speed of the target vehicle with a pre-generated vehicle vector field to determine whether the target vehicle is abnormal.
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