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公开(公告)号：EP3686778A1
公开(公告)日：2020-07-29
申请号：EP19207717.0
申请日：2019-11-07
申请人： Stradvision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
IPC分类号： G06K9/00
摘要： A learning method for improving segmentation performance to be used for detecting road user events including pedestrian events and vehicle events using double embedding configuration in a multi-camera system is provided. The learning method includes steps of: a learning device instructing similarity convolutional layer to generate similarity embedding feature by applying similarity convolution operations to a feature outputted from a neural network; instructing similarity loss layer to output a similarity loss by referring to a similarity between two points sampled from the similarity embedding feature, and its corresponding GT label image; instructing distance convolutional layer to generate distance embedding feature by applying distance convolution operations to the similarity embedding feature; instructing distance loss layer to output a distance loss for increasing inter-class differences among mean values of instance classes and decreasing intra-class variance values of the instance classes; backpropagating at least one of the similarity loss and the distance loss.
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42.发明公开
公开(公告)号：EP3637329A1
公开(公告)日：2020-04-15
申请号：EP19184966.0
申请日：2019-07-08
申请人： Stradvision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for learning a neural network by adjusting a learning rate each time when an accumulated number of iterations reaches one of a first to an n-th specific values. The method includes steps of: (a) a learning device, while increasing k from 1 to (n-1), (b1) performing a k-th learning process of repeating the learning of the neural network at a k-th learning rate by using a part of the training data while the accumulated number of iterations is greater than a (k-1)-th specific value and is equal to or less than a k-th specific value, (b2) changing a k-th gamma to a (k+1)-th gamma by referring to k-th losses of the neural network which are obtained by the k-th learning process and (ii) changing a k-th learning rate to a (k+1)-th learning rate by using the (k+1)-th gamma.
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43.发明公开
公开(公告)号：EP3633618A1
公开(公告)日：2020-04-08
申请号：EP19184979.3
申请日：2019-07-08
申请人： Stradvision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for tracking an object by using a CNN including a tracking network is provided. The method includes steps of:
a testing device
(a) generating a feature map by using a current video frame, and instructing an RPN to generate information on proposal boxes;
(b)
(i) generating an estimated state vector by using a Kalman filter algorithm, generating an estimated bounding box, and determining a specific proposal box as a seed box, and
(ii) instructing an FCN to apply full convolution operations to the feature map, to thereby output a position sensitive score map;
(c) generating a current bounding box by referring to a regression delta and a seed box which are generated by instructing a pooling layer to pool a region, corresponding to the seed box, on the position sensitive score map, and adjusting the current bounding box by using the Kalman filter algorithm.-
公开(公告)号：EP3483793A1
公开(公告)日：2019-05-15
申请号：EP18192804.5
申请日：2018-09-05
申请人： Stradvision, Inc.
发明人： Kim, Yongjoong , Nam, Woonhyun , Boo, Sukhoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for configuring a CNN with learned parameters that performs activation operation of an activation module and convolution operation of one or more convolutional layer in a convolutional layer at the same time is provided. The method includes steps of: (a) allowing a comparator to compare an input value corresponding to each of pixel values of an input image as a test image with a predetermined reference value and then output a comparison result; (b) allowing a selector to output a specific parameter corresponding to the comparison result among multiple parameters of the convolutional layer; and (c) allowing a multiplier to output a multiplication value calculated by multiplying the specific parameter by the input value and allowing the multiplication value to be determined as a result value acquired by applying the convolutional layer to an output of the activation module.
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公开(公告)号：EP3471022A1
公开(公告)日：2019-04-17
申请号：EP18192813.6
申请日：2018-09-05
申请人： StradVision, Inc.
发明人： Kim, Yongjoong , Nam, Woonhyun , Boo, Sukhoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A learning method for acquiring a bounding box corresponding to an object in a training image from multi-scaled feature maps by using a CNN is provided. The learning method includes steps of: (a) allowing an N-way RPN to acquire at least two specific feature maps and allowing the N-way RPN to apply certain operations to the at least two specific feature maps; (b) allowing an N-way pooling layer to generate multiple pooled feature maps by applying pooling operations to respective areas on the at least two specific feature maps; and (c) (i) allowing a FC layer to acquire information on pixel data of the bounding box, and (ii) allowing a loss layer to acquire first comparative data, thereby adjusting at least one of parameters of the CNN by using the first comparative data during a backpropagation process.
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