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公开(公告)号：EP3690733A1
公开(公告)日：2020-08-05
申请号：EP20153663.8
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
摘要： A method for supporting at least one administrator to evaluate detecting processes of object detectors to provide logical grounds of an autonomous driving is provided. And the method includes steps of: (a) a computing device instructing convolutional layers, included in an object detecting CNN which has been trained before, to generate reference convolutional feature maps by applying convolutional operations to reference images inputted thereto, and instructing ROI pooling layers included therein to generate reference ROI-Pooled feature maps by pooling at least part of values corresponding to ROIs on the reference convolutional feature maps; and (b) the computing device instructing a representative selection unit to classify the reference ROI-Pooled feature maps by referring to information on classes of objects included in their corresponding ROIs on the reference images, and to generate at least one representative feature map per each class.
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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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73.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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74.发明公开
公开(公告)号：EP3690721A1
公开(公告)日：2020-08-05
申请号：EP20152842.9
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 method for face recognition by using a multiple patch combination based on a deep neural network is provided. The method includes steps of: a face-recognizing device, (a) if a face image with a 1-st size is acquired, inputting the face image into a feature extraction network, to allow the feature extraction network to generate a feature map by applying convolution operation to the face image with the 1-st size, and to generate multiple features by applying sliding-pooling operation to the feature map, wherein the feature extraction network has been learned to extract a feature using a face image for training having a 2-nd size and wherein the 2-nd size is smaller than the 1-st size; and (b) inputting the multiple features into a learned neural aggregation network, to allow the neural aggregation network to aggregate the multiple features and to output an optimal feature for the face recognition.
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公开(公告)号：EP3690720A1
公开(公告)日：2020-08-05
申请号：EP20152822.1
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
IPC分类号： G06K9/00
摘要： A method for detecting body information on passengers of a vehicle based on humans' status recognition is provided. The method includes steps of: a passenger body information-detecting device, (a) inputting an interior image of the vehicle into a face recognition network, to detect faces of the passengers and output passenger feature information, and inputting the interior image into a body recognition network, to detect bodies and output body-part length information; and (b) retrieving specific height mapping information by referring to a height mapping table of ratios of segment body portions of human groups to heights per the human groups, acquiring a specific height of the specific passenger, retrieving specific weight mapping information from a weight mapping table of correlations between the heights and weights per the human groups, and acquiring a weight of the specific passenger by referring to the specific height.
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公开(公告)号：EP3690717A1
公开(公告)日：2020-08-05
申请号：EP20152475.8
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 learning method for detecting unoccupied parking spaces by using probability distributions on decision points of the unoccupied parking spaces and relational linear segment information on relationships among the decision points is provided. And the method includes steps of: (a) a learning device performing (i) a process of instructing a first CNN to apply a first CNN regression operation to a parking circumstance image, to thereby calculate each of one or more estimated probability distributions, and (ii) a process of instructing a second CNN to apply a second CNN regression operation to the parking circumstance image, to thereby generate estimated relational linear segment information; and (b) the learning device instructing a loss layer to perform (i) a process training parameters in the first CNN by performing backpropagation using a first loss, and (ii) a process of training of parameters in the second CNN by performing backpropagation using a second loss.
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公开(公告)号：EP3690706A1
公开(公告)日：2020-08-05
申请号：EP20151267.0
申请日：2020-01-10
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 detecting lane elements, which are unit regions including pixels of lanes in an input image, to plan the drive path of an autonomous vehicle by using a horizontal filter mask is provided. The method includes steps of: a computing device acquiring a segmentation score map from a CNN using the input image; instructing a post-processing module, capable of performing data processing at an output end of the CNN, to generate a magnitude map by using the segmentation score map and the horizontal filter mask; instructing the post-processing module to determine each of lane element candidates per each of rows of the segmentation score map by referring to values of the magnitude map; and instructing the post-processing module to apply estimation operations to each of the lane element candidates per each of the rows, to thereby detect each of the lane elements.
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公开(公告)号：EP3686837A1
公开(公告)日：2020-07-29
申请号：EP20151677.0
申请日：2020-01-14
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 reduction of distortion occurred in a warped image by using a GAN is provided for enhancing fault tolerance and fluctuation robustness in extreme situations. And the method includes steps of: (a) if an initial image is acquired, instructing an adjusting layer included in the generating network to adjust at least part of initial feature values, to thereby transform the initial image into an adjusted image; and (b) if at least part of (i) a naturality score, (ii) a maintenance score, and (iii) a similarity score are acquired, instructing a loss layer included in the generating network to generate a generating network loss by referring to said at least part of the naturality score, the maintenance score and the similarity score, and learn parameters of the generating network. Further, the method can be used for estimating behaviors, and detecting or tracking objects with high precision, etc.
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公开(公告)号：EP3686808A1
公开(公告)日：2020-07-29
申请号：EP19215137.1
申请日：2019-12-11
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： There is provided a method for transforming convolutional layers of a CNN including m convolutional blocks to optimize CNN parameter quantization to be used for mobile devices, compact networks, and the like with high precision via hardware optimization. The method includes steps of: a computing device (a) generating k-th quantization loss values by referring to k-th initial weights of a k-th initial convolutional layer included in a k-th convolutional block, a (k-1)-th feature map outputted from the (k-1)-th convolutional block, and each of k-th scaling parameters; (b) determining each of k-th optimized scaling parameters by referring to the k-th quantization loss values; (c) generating a k-th scaling layer and a k-th inverse scaling layer by referring to the k-th optimized scaling parameters; and (d) transforming the k-th initial convolutional layer into a k-th integrated convolutional layer by using the k-th scaling layer and the (k-1)-th inverse scaling layer.
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公开(公告)号：EP3686801A1
公开(公告)日：2020-07-29
申请号：EP20151240.7
申请日：2020-01-10
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 a CNN for image recognition is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (a) instructing a first transposing layer or a pooling layer to generate an integrated feature map by concatenating pixels, per each ROI, on pooled ROI feature maps; (b) instructing a 1×H1 convolutional layer to generate a first adjusted feature map using a first reshaped feature map, generated by concatenating features in H1 channels of the integrated feature map, and instructing a 1×H2 convolutional layer to generate a second adjusted feature map using a second reshaped feature map, generated by concatenating features in H2 channels of the first adjusted feature map; and (c) instructing a second transposing layer or a classifying layer to divide the second adjusted feature map by each pixel, to thereby generate pixel-wise feature maps.
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