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公开(公告)号：EP3657382A1
公开(公告)日：2020-05-27
申请号：EP19195508.7
申请日：2019-09-05
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： G06K9/00
摘要： A method for warning a vehicle of a risk of lane change is provided. The method includes steps of: (a) an alarm device, if at least one rear image captured by a running vehicle is acquired, segmenting the rear image by using a learned convolutional neural network (CNN) to thereby obtain a segmentation image corresponding to the rear image; (b) the alarm device checking at least one free space ratio in at least one blind spot by referring to the segmentation image, wherein the free space ratio is determined as a ratio of a road area without an object in the blind spot to a whole area of the blind spot; and (c) the alarm device, if the free space ratio is less than or equal to at least one predetermined threshold value, warning a driver of the vehicle of the risk of lane change.
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22.发明公开
公开(公告)号：EP3633558A1
公开(公告)日：2020-04-08
申请号：EP19195517.8
申请日：2019-09-05
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： G06N3/04 , G06N3/08 , G06N5/00 , G08G1/16 , G06K9/00 , G06K9/32 , G06K9/46 , G06K9/62 , B60W40/00
摘要： A learning method of a CNN (Convolutional Neural Network) for monitoring one or more blind spots of a monitoring vehicle is provided. The learning method includes steps of: a learning device instructing a detector to output class information and location information on a monitored vehicle in a training image; instructing a cue information extracting layer to output cue information on the monitored vehicle by using the outputted information, and instructing an FC layer to determine whether the monitored vehicle is located on the blind spots by neural-network operations with the cue information or its processed values; and learning parameters of the FC layer and parameters of the detector, by backpropagating loss values for the blind spots by referring to the determination and its corresponding GT and backpropagating loss values for the vehicle detection by referring to the class information and the location information and their corresponding GT, respectively.
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23.发明公开
公开(公告)号：EP3624015A1
公开(公告)日：2020-03-18
申请号：EP19172862.5
申请日：2019-05-06
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 learning method for a CNN (Convolutional Neural Network) capable of encoding at least one training image with multiple feeding layers, wherein the CNN includes a 1st to an n-th convolutional layers, which respectively generate a 1st to an n-th main feature maps by applying convolution operations to the training image, and a 1st to an h-th feeding layers respectively corresponding to h convolutional layers (1≤h≤ (n-1)) is provided. The learning method includes steps of: a learning device instructing the convolutional layers to generate the 1st to the n-th main feature maps, wherein the learning device instructs a k-th convolutional layer to acquire a (k-1)-th main feature map and an m-th sub feature map, and to generate a k-th main feature map by applying the convolution operations to the (k-1)-th integrated feature map generated by integrating the (k-1)-th main feature map and the m-th sub feature map.
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24.发明公开
公开(公告)号：EP3623991A1
公开(公告)日：2020-03-18
申请号：EP19172864.1
申请日：2019-05-06
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 learning method of a CNN capable of detecting one or more lanes is provided. The learning method includes steps of: a learning device (a) applying convolution operations to an image, to generate a feature map, and generating lane candidate information; (b) generating a first pixel data map including information on pixels in the image and their corresponding pieces of first data, wherein main subsets from the first data include distance values from the pixels to their nearest first lane candidates by using a direct regression, and generating a second pixel data map including information on the pixels and their corresponding pieces of second data, wherein main subsets from the second data include distance values from the pixels to their nearest second lane candidates by using the direct regression; and (c) detecting the lanes by inference to the first pixel data map and the second pixel data map.
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25.发明公开
公开(公告)号：EP3620980A1
公开(公告)日：2020-03-11
申请号：EP19178056.8
申请日：2019-06-04
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 learning method of a CNN for detecting lanes is provided. The method includes steps of: a learning device (a) instructing convolutional layers to generate feature maps by applying convolution operations to an input image from an image data set; (b) instructing an FC layer to generate an estimated result vector of cluster ID classifications of the lanes by feeding a specific feature map among the feature maps into the FC layer; and (c) instructing a loss layer to generate a classification loss by referring to the estimated result vector and a cluster ID GT vector, and backpropagate the classification loss, to optimize device parameters of the CNN; wherein the cluster ID GT vector is GT information on probabilities of being cluster IDs per each of cluster groups assigned to function parameters of a lane modeling function by clustering the function parameters based on information on the lanes.
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公开(公告)号：EP3620977A1
公开(公告)日：2020-03-11
申请号：EP19171149.8
申请日：2019-04-25
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 generating at least one training data set including steps of: (a) a computing device acquiring (i) an original image and (ii) an initial synthesized label generated by using an original label and a bounding box corresponding to an arbitrary specific object (b) the computing device supporting a CNN module to generate a first synthesized image and a first synthesized label by using the original image and the initial synthesized label, wherein the first synthesized label is created by adding a specific label to the original label at a location in the original label corresponding to a location of the bounding box in the initial synthesized label, and wherein the first synthesized image is created by adding a specific image o to the original image at a location in the original image corresponding to the location of the bounding box in the initial synthesized label.
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公开(公告)号：EP3620956A1
公开(公告)日：2020-03-11
申请号：EP19172860.9
申请日：2019-05-06
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 learning method for detecting at least one lane based on a convolutional neural network (CNN) is provided. The learning method includes steps of: (a) a learning device obtaining encoded feature maps, and information on lane candidate pixels in a input image; (b) the learning device, classifying a first parts of the lane candidate pixels ,whose probability scores are not smaller than a predetermined threshold, as strong line pixels, and classifying the second parts of the lane candidate pixels, whose probability scores are less than the threshold but not less than another predetermined threshold, as weak lines pixels; and (c) the learning device, if distances between the weak line pixels and the strong line pixels are less than a predetermined distance, classifying the weak line pixels as pixels of additional strong lines, and determining that the pixels of the strong line and the additional correspond to pixels of the lane.
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公开(公告)号：EP3620954A1
公开(公告)日：2020-03-11
申请号：EP19171092.0
申请日：2019-04-25
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 generating at least one data set for learning to be used for detecting at least one obstruction in autonomous driving circumstances is provided. The method includes steps of: a computing device (a) obtaining a first original image indicating a driving situation, and a first segmentation ground truth (GT) image corresponding to the first original image; (b) obtaining a second original image including a specific object, and a second segmentation GT image which includes segmentation information for the specific object and corresponds to the second original image; (c) obtaining a third original image by cutting a portion corresponding to the specific object, and a third segmentation GT image by cutting pixels corresponding to an area where the specific object is located; and (d) creating the data set for learning which includes a fourth original image and a fourth segmentation GT image corresponding to the fourth original image.
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公开(公告)号：EP3471026A1
公开(公告)日：2019-04-17
申请号：EP18192821.9
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, Taewong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
IPC分类号： G06N3/04
摘要： A method for acquiring a bounding box corresponding to an object is provided. The method includes steps of: (a) acquiring proposal boxes; (b) selecting specific proposal box among the proposal boxes by referring to (i) a result of comparing distance between a reference bounding box and the proposal boxes and/or (ii) a result of comparing score which indicates whether the proposal boxes includes the object, and then setting the specific proposal box as a starting area of a tracking box; (c) determining a specific area of the current frame as a target area of the tracking box by using the mean shift tracking algorithm; and (d) allowing a pooling layer to generate a pooled feature map by applying pooling operation to an area corresponding to the specific area and then allowing a FC layer to acquire a bounding box by applying regression operation to the pooled feature map.
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30.发明公开
公开(公告)号：EP3467774A1
公开(公告)日：2019-04-10
申请号：EP18192824.3
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 tracking a target object in frames of video data using Absorbing Markov Chain (AMC), including steps of: (a) acquiring a bounding box containing the target object in a current frame and a segmentation result for the target object in a previous frame; (b) obtaining obtain a region of interest (ROI) in the current frame by enlarging the bounding box to contain a portion of background information surrounding the target object; (c) acquiring information on local regions within the ROI in the current frame; (d) constructing an AMC graph using at least part of the local regions within the region of interest (ROI) in the current frame and local regions within a region of interest (ROI) in the previous frame; and (e) acquiring a segmentation result for the target object within the current frame by thresholding individual nodes in the AMC graph using absorption times thereof.
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