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31.发明公开
公开(公告)号：EP3690729A1
公开(公告)日：2020-08-05
申请号：EP20153495.5
申请日：2020-01-24
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for warning by detecting an abnormal state of a driver of a vehicle based on deep learning is provided. The method includes steps of: a driver state detecting device (a) inputting an interior image of the vehicle into a drowsiness detecting network, to detect a facial part of the driver, detect an eye part from the facial part, detect a blinking state of an eye to determine a drowsiness state, and inputting the interior image into a pose matching network, to detect body keypoints of the driver, determine whether the body keypoints match one of preset driving postures, to determine the abnormal state; and (b) if the driver is in a hazardous state referring to part of the drowsiness state and the abnormal state, transmitting information on the hazardous state to nearby vehicles over vehicle-to-vehicle communication to allow nearby drivers to perceive the hazardous state.
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公开(公告)号：EP3686785A1
公开(公告)日：2020-07-29
申请号：EP19220237.2
申请日：2019-12-31
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 parameters of an object detector by using a target object estimating network adaptable to customers' requirements such as KPI is provided. When a focal length or a resolution changes depending on the KPI, scales of objects also change. In this method for customer optimizable design, unsecure objects such as falling or fallen objects may be detected more accurately, and also fluctuations of the objects may be detected. Therefore, the method can be usefully performed for military purpose or for detection of the objects at distance. The method includes steps of: a learning device instructing an RPN to generate k-th object proposals on k-th manipulated images which correspond to (k-1)-th target region on an image; instructing an FC layer to generate object detection information corresponding to k-th objects; and instructing an FC loss layer to generate FC losses, by increasing k from 1 to n.
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33.发明公开
公开(公告)号：EP3633550A1
公开(公告)日：2020-04-08
申请号：EP19184961.1
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 parameters of an object detector based on R-CNN is provided. The method includes steps of: a learning device (a) if training image is acquired, instructing (i) convolutional layers to generate feature maps by applying convolution operations to the training image, (ii) an RPN to output ROI regression information and matching information (iii) a proposal layer to output ROI candidates as ROI proposals by referring to the ROI regression information and the matching information, and (iv) a proposal-selecting layer to output the ROI proposals by referring to the training image; (b) instructing pooling layers to generate feature vectors by pooling regions in the feature map, and instructing FC layers to generate object regression information and object class information; and (c) instructing first loss layers to calculate and backpropagate object class loss and object regression loss, to thereby learn parameters of the FC layers and the convolutional layers.
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公开(公告)号：EP3477554A3
公开(公告)日：2019-06-05
申请号：EP18192822.7
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 adjusting parameters of a CNN using loss augmentation is provided. The method includes steps of: a learning device acquiring (a) a feature map from a training image; (b) (i) proposal ROIs corresponding to an object using an RPN, and a first pooled feature map by pooling areas, on the feature map, corresponding to the proposal ROIs, and (ii) a GT ROI, on the training image, corresponding to the object, and a second pooled feature map by pooling an area, on the feature map, corresponding to the GT ROI; and (c) (i) information on pixel data of a first bounding box when the first and second pooled feature maps are inputted into an FC layer, (ii) comparative data between the information on the pixel data of the first bounding box and a GT bounding box, and backpropagating information on the comparative data to adjust the parameters.
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35.发明公开
公开(公告)号：EP3467713A8
公开(公告)日：2019-06-05
申请号：EP18192803.7
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 improving image segmentation by using a learning device is disclosed. The method includes steps of: (a) if a training image is obtained, acquiring (2- K) th to (2-1) th feature maps through an encoding layer and a decoding layer, and acquiring 1 st to H th losses from the 1 st to the H th loss layers respectively corresponding to H feature maps, obtained from the H filters, among the (2-K) th to the (2-1) th feature maps; and (b) upon performing a backpropagation process, performing processes of allowing the (2-M) th filter to apply a convolution operation to (M-1) 2 -th adjusted feature map relayed from the (2-(M-1)) th filter to obtain M 1 -th temporary feature map; relaying, to the (2-(M+1)) th filter, M 2 -th adjusted feature map obtained by computing the M th loss with the M 1 -th temporary feature map; and adjusting at least part of parameters of the (1-1) th to the (1-K) th filters and the (2-K) th to the (2-1) th filters.
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公开(公告)号：EP3467772A1
公开(公告)日：2019-04-10
申请号：EP18192807.8
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 segmenting an image using a CNN is provided. The method includes steps of: a segmentation device acquiring (i) a first segmented image for a t -th frame by a CNN_PREVIOUS, having at least one first weight learned at a t -( i +1)-th frame, segmenting the image, (ii) optical flow images corresponding to the ( t -1)-th to the ( ti )-th frames, including information on optical flows from pixels of the first segmented image to corresponding pixels of segmented images of the ( t -1)-th to the ( t-i ) - th frames, and (iii) warped images for the t -th frame by replacing pixels in the first segmented image with pixels in the segmented images referring to the optical flow images, (iv) losses by comparing the first segmented image with the warped images, (v) a CNN_CURRENT with at least one second weight obtained by adjusting the first weight to thereby segment an image of the t -th frame and frames thereafter by using the CNN_CURRENT.
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公开(公告)号：EP3699886A2
公开(公告)日：2020-08-26
申请号：EP20152435.2
申请日：2020-01-17
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for giving a warning on a blind spot of a vehicle based on V2V communication is provided. The method includes steps of: (a) if a rear video of a first vehicle is acquired from a rear camera, a first blind-spot warning device transmitting the rear video to a blind-spot monitor, to determine whether nearby vehicles are in the rear video using a CNN, and output first blind-spot monitoring information of determining whether the nearby vehicles are in a blind spot; and (b) if second blind-spot monitoring information of determining whether a second vehicle is in the blind spot, is acquired from a second blind-spot warning device of the second vehicle, over the V2V communication, the first blind-spot warning device warning that one of the second vehicle and the nearby vehicles is in the blind spot by referring to the first and the second blind-spot monitoring information.
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公开(公告)号：EP3699814A1
公开(公告)日：2020-08-26
申请号：EP20153516.8
申请日：2020-01-24
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
IPC分类号： G06K9/00
摘要： A method for adjusting a position of a driver assistance device according to a driver state is provided. The method includes steps of: a position adjusting device, (a) inputting an upper and a lower body images of a driver, acquired after the driver sits and starts a vehicle, into a pose estimation network, to acquire body keypoints, calculate body part lengths, and adjust a driver's seat position; and (b) while the vehicle is traveling, inputting the upper body image into a face detector to detect a facial part, inputting the facial part into an eye detector to detect an eye part, and inputting the adjusted driver's seat position and 2D coordinates of an eye into a 3D coordinates transforming device, to generate 3D coordinates of the eye referring to the 2D coordinates and the driver's seat position, and adjust a mirror position of the vehicle referring to the 3D coordinates.
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公开(公告)号：EP3690741A2
公开(公告)日：2020-08-05
申请号：EP20152002.0
申请日：2020-01-15
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： G06K9/62
摘要： A method for evaluating a reliability of labeling training images to be used for learning a deep learning network is provided. The method includes steps of: a reliability-evaluating device instructing a similar-image selection network to select validation image candidates with their own true labels having shooting environments similar to those of acquired original images, which are unlabeled images, and instructing an auto-labeling network to auto-label the validation image candidates with their own true labels and the original images; and (i) evaluating a reliability of the auto-labeling network by referring to true labels and auto labels of easy-validation images, and (ii) evaluating a reliability of a manual-labeling device by referring to true labels and manual labels of difficult-validation images. This method can be used to recognize surroundings by applying a bag-of-words model, to optimize sampling processes for selecting a valid image among similar images, and to reduce annotation costs.
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公开(公告)号：EP3686799A1
公开(公告)日：2020-07-29
申请号：EP19220233.1
申请日：2019-12-31
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 of neural network operations by using a grid generator is provided for converting modes according to classes of areas to satisfy level 4 of autonomous vehicles. The method includes steps of: a computing device (a) instructing a detector to acquire object location information for testing and class information; (b) instructing the grid generator to generate section information by referring to the object location information for testing; (c) instructing a neural network to determine parameters for testing, to be used for applying the neural network operations to either (i) the subsections including each of the objects for testing and each of non-objects for testing, or (ii) each of sub-regions, in each of the subsections, where said each of the non-objects for testing is located; and (d) instructing the neural network to apply the neural network operations to the test image for testing to thereby generate neural network outputs.
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