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1.发明申请公开(公告)号：US20190251383A1
公开(公告)日：2019-08-15
申请号：US16394062
申请日：2019-04-25
Inventor： GREGORY SENAY , SOTARO TSUKIZAWA , MIN YOUNG KIM , LUCA RIGAZIO
Abstract: Inputting an image to a neural network, performing convolution on a current frame included in the image to calculate a current feature map, which is a feature map at a present time, combining a past feature map, which is obtained by performing convolution on a past frame included in the image, and the current feature map, estimating an object candidate area using the combined past feature map and current feature map, estimating positional information and identification information regarding the one or more objects included in the current frame using the combined past feature map and current feature map and the estimated object candidate area, and outputting the positional information and the identification information regarding the one or more objects included in the current frame of the image estimated in the estimating as object detection results are included.
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公开(公告)号：US20170220891A1
公开(公告)日：2017-08-03
申请号：US15485250
申请日：2017-04-12
Inventor： MIN YOUNG KIM , LUCA RIGAZIO , SOTARO TSUKIZAWA , KAZUKI KOZUKA
CPC classification number: G06K9/4628 , G06K9/6223 , G06K9/6255 , G06K9/6256 , G06K9/66 , G06N3/0454 , G06N3/082 , G06N7/005
Abstract: A determination method for determining the structure of a convolutional neural network includes acquiring N filters having the weights trained using a training image group as the initial values, where N is a natural number greater than or equal to 1, and increasing the number of the filters from N to M, where M is a natural number greater than or equal to 2 and is greater than N, by adding a filter obtained by performing a transformation used in image processing fields on at least one of the N filters.
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