- Patent Title: Learning method and learning device for object detector to be used for surveillance based on convolutional neural network capable of converting modes according to scales of objects, and testing method and testing device using the same
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Application No.: US16258248Application Date: 2019-01-25
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Publication No.: US10402686B1Publication Date: 2019-09-03
- 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 ; G06K9/62

Abstract:
A method for an object detector to be used for surveillance based on a convolutional neural network capable of converting modes according to scales of objects is provided. The method includes steps of: a learning device (a) instructing convolutional layers to output a feature map by applying convolution operations to an image and instructing an RPN to output ROIs in the image; (b) instructing pooling layers to output first feature vectors by pooling each of ROI areas on the feature map per each of their scales, instructing first FC layers to output second feature vectors, and instructing second FC layers to output class information and regression information; and (c) instructing loss layers to generate class losses and regression losses by referring to the class information, the regression information, and their corresponding GTs.
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