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公开(公告)号：US10803602B2
公开(公告)日：2020-10-13
申请号：US16171923
申请日：2018-10-26
Inventor： Min Young Kim , Sotaro Tsukizawa
Abstract: An object tracking method includes inputting, into a neural network, two or more chronologically consecutive images, and matching similarity by comparing features extracted by the neural network, namely features of each of the two or more input images, and thereby outputting, as an identification result, identification information and position information about one or more objects depicted in a chronologically later image than a chronologically earlier image, which match one or more objects which are tracking candidates depicted in the chronologically earlier image. The neural network includes two or more identical structures having zero or more fully-connected layers and one or more convolution layers, and shares parameters among corresponding layers across the identical structures.
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公开(公告)号：US10558885B2
公开(公告)日：2020-02-11
申请号：US15485250
申请日：2017-04-12
Inventor： Min Young Kim , Luca Rigazio , Sotaro Tsukizawa , Kazuki Kozuka
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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公开(公告)号：US10796184B2
公开(公告)日：2020-10-06
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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