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公开(公告)号：US09947228B1
公开(公告)日：2018-04-17
申请号：US15725394
申请日：2017-10-05
申请人： StradVision, Inc.
发明人： Yongjoong Kim , Woonhyun Nam , Sukhoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
CPC分类号： G08G1/167 , B60Q9/008 , G06K9/00805 , G06K9/34 , G06K9/4623 , G06K9/481 , G08G1/04
摘要： A method of monitoring a blind spot of a monitoring vehicle by using a blind spot monitor is provided. The method includes steps of: the blind spot monitor (a) acquiring a feature map from rear video images, on condition that video images with reference vehicles in the blind spot are acquired, reference boxes for the reference vehicles are created, and the reference boxes are set as proposal boxes; (b) acquiring feature vectors for the proposal boxes on the feature map by pooling, inputting the feature vectors into a fully connected layer, acquiring classification and regression information; and (c) selecting proposal boxes by referring to the classification information, acquiring bounding boxes for the proposal boxes by using the regression information, determining the pose of the monitored vehicle corresponding to each of the bounding boxes, and determining whether a haphazard vehicle is located in the blind spot of the monitoring vehicle.
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公开(公告)号：US09946960B1
公开(公告)日：2018-04-17
申请号：US15783442
申请日：2017-10-13
申请人： StradVision, Inc.
发明人： Yongjoong Kim , Woonhyun Nam , Sukhoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
CPC分类号： G06K9/6256 , G06K9/4642 , G06K9/6202 , G06K9/6215 , G06K9/6267 , G06K2209/21 , G06N3/04 , G06N3/0454 , G06N3/08 , G06N3/084 , G06T7/248 , G06T2210/12
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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33.发明授权公开(公告)号：US09934440B1
公开(公告)日：2018-04-03
申请号：US15724544
申请日：2017-10-04
申请人： StradVision, Inc.
发明人： Yongjoong Kim , Woonhyun Nam , Sukhoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
CPC分类号： G06K9/00791 , B60R1/00 , B60R2300/802 , B60Y2400/3015 , G06K9/2054 , G06K9/481 , G06K9/6256 , G06K9/6267 , G06K9/78 , G06K2209/21 , G06N3/04 , G06N3/0454 , G06N3/08
摘要： A method of monitoring a blind spot of a monitoring vehicle by using a blind spot monitor. The method includes steps of: the blind spot monitor (a) acquiring a feature map from rear video images, on condition that video images with reference vehicles in the blind spot are acquired, reference boxes for the reference vehicles are created, and the reference boxes are set as proposal boxes; (b) acquiring feature vectors for the proposal boxes on the feature map by pooling, inputting the feature vectors into a fully connected layer, acquiring classification and regression information; and (c) selecting proposal boxes by referring to the classification information, acquiring bounding boxes for the proposal boxes by using the regression information, confirming whether the bounding boxes match their corresponding proposal boxes, and determining whether the monitored vehicle is in the proposal boxes to determine the monitored vehicle is in the blind spot.
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公开(公告)号：US10984262B2
公开(公告)日：2021-04-20
申请号：US16153972
申请日：2018-10-08
申请人： StradVision, Inc.
发明人： 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
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, if training data corresponding to output from a detector on the monitoring vehicle is inputted, instructing a cue information extracting layer to uses class information and location information on a monitored vehicle included in the training data, thereby outputting cue information on the monitored vehicle; instructing an FC layer for monitoring the blind spots to perform neural network operations by using the cue information, thereby outputting a result of determining whether the monitored vehicle is located on one of the blind spots; and instructing a loss layer to generate loss values by referring to the result and its corresponding GT, thereby learning parameters of the FC layer for monitoring the blind spots by backpropagating the loss values.
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公开(公告)号：US10824151B2
公开(公告)日：2020-11-03
申请号：US16738320
申请日：2020-01-09
申请人： StradVision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Hak-Kyoung Kim , Woonhyun Nam , Sukhoon Boo , Myungchul Sung , Dongsoo Shin , Donghun Yeo , Wooju Ryu , Myeong-Chun Lee , Hyungsoo Lee , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for providing a dynamic adaptive deep learning model other than a fixed deep learning model, to thereby support at least one specific autonomous vehicle to perform a proper autonomous driving according to surrounding circumstances is provided. And the method includes steps of: (a) a managing device which interworks with autonomous vehicles instructing a fine-tuning system to acquire a specific deep learning model to be updated; (b) the managing device inputting video data and its corresponding labeled data to the fine-tuning system as training data, to thereby update the specific deep learning model; and (c) the managing device instructing an automatic updating system to transmit the updated specific deep learning model to the specific autonomous vehicle, to thereby support the specific autonomous vehicle to perform the autonomous driving by using the updated specific deep learning model other than a legacy deep learning model.
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36.发明授权公开(公告)号：US10796434B1
公开(公告)日：2020-10-06
申请号：US16739168
申请日：2020-01-10
申请人： StradVision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Hak-Kyoung Kim , Woonhyun Nam , Sukhoon Boo , Myungchul Sung , Dongsoo Shin , Donghun Yeo , Wooju Ryu , Myeong-Chun Lee , Hyungsoo Lee , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
IPC分类号： G06K9/00 , G06T7/11 , G08G1/14 , G06K9/62 , B60R11/04 , G06N3/08 , B62D15/02 , B60Q5/00 , B60Q9/00
摘要： A method for learning an automatic parking device of a vehicle for detecting an available parking area is provided. The method includes steps of: a learning device, (a) if a parking lot image of an area nearby the vehicle is acquired, (i) inputting the parking lot image into a segmentation network to output a convolution feature map via an encoder, output a deconvolution feature map by deconvoluting the convolution feature map via a decoder, and output segmentation information by masking the deconvolution feature map via a masking layer; (b) inputting the deconvolution feature map into a regressor to generate relative coordinates of vertices of a specific available parking region, and generate regression location information by regressing the relative coordinates; and (c) instructing a loss layer to calculate 1-st losses by referring to the regression location information and an ROI GT, and learning the regressor via backpropagation using the 1-st losses.
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公开(公告)号：US20200250541A1
公开(公告)日：2020-08-06
申请号：US16738680
申请日：2020-01-09
申请人： StradVision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Hak-Kyoung Kim , Woonhyun Nam , Sukhoon Boo , Myungchul Sung , Dongsoo Shin , Donghun Yeo , Wooju Ryu , Myeong-Chun Lee , Hyungsoo Lee , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A learning method for supporting a safer autonomous driving through a fusion of information acquired from images and communications is provided. And the method includes steps of: (a) a learning device instructing a first neural network and a second neural network to generate an image-based feature map and a communication-based feature map by using a circumstance image and circumstance communication information; (b) the learning device instructing a third neural network to apply a third neural network operation to the image-based feature map and the communication-based feature map to generate an integrated feature map; (c) the learning device instructing a fourth neural network to apply a fourth neural network operation to the integrated feature map to generate estimated surrounding motion information; and (d) the learning device instructing a first loss layer to train parameters of the first to the fourth neural networks.
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公开(公告)号：US10474930B1
公开(公告)日：2019-11-12
申请号：US16152699
申请日：2018-10-05
申请人： StradVision, Inc.
发明人： 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
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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公开(公告)号：US10402724B2
公开(公告)日：2019-09-03
申请号：US15723538
申请日：2017-10-03
申请人： StradVision, Inc.
发明人： Yongjoong Kim , Woonhyun Nam , Sukhoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for acquiring a pseudo-3D box from a 2D bounding box in a training image is provided. The method includes steps of: (a) a computing device acquiring the training image including an object bounded by the 2D bounding box; (b) the computing device performing (i) a process of classifying a pseudo-3D orientation of the object, by referring to information on probabilities corresponding to respective patterns of pseudo-3D orientation and (ii) a process of acquiring 2D coordinates of vertices of the pseudo-3D box by using regression analysis; and (c) the computing device adjusting parameters thereof by backpropagating loss information determined by referring to at least one of (i) differences between the acquired 2D coordinates of the vertices of the pseudo-3D box and 2D coordinates of ground truth corresponding to the pseudo-3D box, and (ii) differences between the classified pseudo-3D orientation and ground truth corresponding to the pseudo-3D orientation.
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公开(公告)号：US10300851B1
公开(公告)日：2019-05-28
申请号：US16151760
申请日：2018-10-04
申请人： StradVision, Inc.
发明人： 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
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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