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公开(公告)号：EP3690817A1
公开(公告)日：2020-08-05
申请号：EP20153532.5
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
IPC分类号： G06T7/80
摘要： A method for enhancing an accuracy of object distance estimation based on a subject camera by performing pitch calibration of the subject camera more precisely with additional information acquired through V2V communication is provided. And the method includes steps of: (a) a computing device, performing (i) a process of instructing an initial pitch calibration module to apply a pitch calculation operation to the reference image, to thereby generate an initial estimated pitch, and (ii) a process of instructing an object detection network to apply a neural network operation to the reference image, to thereby generate reference object detection information; (b) the computing device instructing an adjusting pitch calibration module to (i) select a target object, (ii) calculate an estimated target height of the target object, (iii) calculate an error corresponding to the initial estimated pitch, and (iv) determine an adjusted estimated pitch on the subject camera by using the error.
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2.发明公开
公开(公告)号：EP3690755A1
公开(公告)日：2020-08-05
申请号：EP20152464.2
申请日：2020-01-17
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 on-device continual learning of a neural network which analyzes input data is provided for smartphones, drones, vessels, or a military purpose. The method includes steps of: a learning device, (a) uniform-sampling new data to have a first volume, instructing a boosting network to convert a k-dimension random vector into a k-dimension modified vector, instructing an original data generator network to repeat outputting synthetic previous data of a second volume corresponding to the k-dimension modified vector and previous data having been used for learning, and generating a batch for a current-learning; and (b) instructing the neural network to generate output information corresponding to the batch. The method can be used for preventing catastrophic forgetting and an invasion of privacy, and for optimizing resources such as storage and sampling processes for training images. Further the method can be performed through a learning for Generative adversarial networks (GANs).
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公开(公告)号：EP3690731A2
公开(公告)日：2020-08-05
申请号：EP20153637.2
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 achieving better performance in an autonomous driving while saving computing powers, by using confidence scores representing a credibility of an object detection which is generated in parallel with an object detection process is provided. And the method includes steps of: (a) a computing device acquiring at least one circumstance image on surroundings of a subject vehicle, through at least one image sensor installed on the subject vehicle; (b) the computing device instructing a Convolutional Neural Network(CNN) to apply at least one CNN operation to the circumstance image, to thereby generate initial object information and initial confidence information on the circumstance image; and (c) the computing device generating final object information on the circumstance image by referring to the initial object information and the initial confidence information with a support of a Reinforcement Learning(RL) agent, and through V2X communications with at least part of surrounding objects.
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4.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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6.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：EP3699886A3
公开(公告)日：2020-11-04
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：EP3690741A3
公开(公告)日：2020-08-19
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：EP3690844A1
公开(公告)日：2020-08-05
申请号：EP20153534.1
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： G08G1/00 , B60W30/165 , G05D1/02
摘要： A method for switching driving modes of a subject vehicle to support the subject vehicle to perform a platoon driving by using platoon driving information is provided. And the method includes steps of: (a) a basement server, which interworks with the subject vehicle driving in a first mode, acquiring first platoon driving information, to N-th platoon driving information by referring to a real-time platoon driving information DB; (b) the basement server (i) calculating a first platoon driving suitability score to an N-th platoon driving suitability score by referring to first platoon driving parameters to N-th platoon driving parameters and (ii) selecting a target platoon driving group to be including the subject vehicle; (c) the basement server instructing the subject vehicle to drive in a second mode.
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公开(公告)号：EP3690726A1
公开(公告)日：2020-08-05
申请号：EP20152976.5
申请日：2020-01-21
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 learning method for transforming a virtual video on a virtual world to a more real-looking video is provided. And the method includes steps of: (a) a learning device instructing a generating CNN to apply a convolutional operation to an N-th virtual training image, N-th meta data and (N-K)-th reference information to generate an N-th feature map; (b) the learning device instructing the generating CNN to apply a deconvolutional operation to the N-th feature map to generate an N-th transformed image; (c) the learning device instructing a discriminating CNN to apply a discriminating CNN operation to the N-th transformed image to generate a category score vector; (d) the learning device instructing the generating CNN to generate a generating CNN loss by referring to the category score vector and its corresponding GT, and to perform backpropagation by referring to the generating CNN loss to learn parameters of the generating CNN.
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