- Patent Title: Learning method and learning device for improving segmentation performance to be used for detecting road user events using double embedding configuration in multi-camera system and testing method and testing device using the same
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Application No.: US16257993Application Date: 2019-01-25
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Publication No.: US10551846B1Publication Date: 2020-02-04
- Inventor: Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
- Applicant: Stradvision, Inc.
- Applicant Address: KR Pohang, Gyeongbuk
- Assignee: Stradvision, Inc.
- Current Assignee: Stradvision, Inc.
- Current Assignee Address: KR Pohang, Gyeongbuk
- Agency: Kaplan Breyer Schwarz, LLP
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G05D1/02 ; G06T7/194 ; G06T7/70 ; G06K9/62 ; G06N3/08 ; G06N5/04 ; G06N20/00

Abstract:
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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