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公开(公告)号：US10733722B2
公开(公告)日：2020-08-04
申请号：US15983342
申请日：2018-05-18
Applicant: NEC Laboratories America, Inc.
Inventor： Alexandru Niculescu-Mizil , Eric Cosatto , Felix Wu
IPC: G06T7/00 , H04N19/132 , H04N19/17 , G16H50/70 , G16H50/20 , G06T11/20 , G16H20/30 , G16H30/40 , G06T11/00
Abstract: Systems and methods for detecting and correcting defective products include capturing at least one image of a product with at least one image sensor to generate an original image of the product. An encoder encodes portions of an image extracted from the original image to generate feature space vectors. A decoder decodes the feature space vectors to reconstruct the portions of the image into reconstructed portions by predicting defect-free structural features in each of the portions according to hidden layers trained to predict defect-free products. Each of the reconstructed portions are merged into a reconstructed image of a defect-free representation of the product. The reconstructed image is communicated to a contrastor to detect anomalies indicating defects in the product.
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公开(公告)号：US10336252B2
公开(公告)日：2019-07-02
申请号：US15400059
申请日：2017-01-06
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto
IPC: B60Q9/00 , B60W30/095 , B60W10/18 , B60W10/04 , B60W10/30 , B60W40/09 , B60W50/14 , B60W40/04 , B60Q5/00 , B60W40/08 , B60W40/06 , B60W50/16
Abstract: Systems and methods are disclosed to assist a driver with a dangerous condition by creating a graph representation where traffic participants and static elements are the vertices and the edges are relations between pairs of vertices; adding attributes to the vertices and edges of the graph based on information obtained on the driving vehicle, the traffic participants and additional information; creating a codebook of dangerous driving situations, each represented as graphs; performing subgraph matching between the graphs in the codebook and the graph representing a current driving situation to select a set of matching graphs from the codebook; determining a distance metric between each selected codebook graphs and the matching subgraph of the current driving situation; from codebook graphs with a low distance, determining potential dangers; and generating an alert if one or more of the codebook dangers are imminent.
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公开(公告)号：US20190197236A1
公开(公告)日：2019-06-27
申请号：US16218976
申请日：2018-12-13
Applicant: NEC Laboratories America, Inc.
Inventor： Alexandru Niculescu-Mizil , Eric Cosatto , Xavier Fontaine
CPC classification number: G06F21/552 , G06F21/554 , G06F2221/034 , G06K9/6256 , G06N3/02 , G06N5/003 , H04L63/1425 , H04L63/1441
Abstract: Methods and systems for detecting and correcting anomalies include predicting normal behavior of a monitored system based on training data that includes only sensor data collected during normal behavior of the monitored system. The predicted normal behavior is compared to recent sensor data to determine that the monitored system is behaving abnormally. A corrective action is performed responsive to the abnormal behavior to correct the abnormal behavior.
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公开(公告)号：US10330787B2
公开(公告)日：2019-06-25
申请号：US15689755
申请日：2017-08-29
Applicant: NEC Laboratories America, Inc.
Inventor： Iain Melvin , Eric Cosatto , Igor Durdanovic , Hans Peter Graf
IPC: G01S13/93 , G06N3/08 , G06K9/46 , G01S17/93 , B60Q9/00 , B60R1/00 , B60W30/09 , G06K9/00 , G06K9/62 , G01S7/20 , G01S7/295 , G01S7/41 , G01S13/86
Abstract: A computer-implemented method and system are provided for driving assistance. The system includes an image capture device configured to capture image data relative to an outward view from a motor vehicle. The system further includes a processor configured to detect and localize objects, in a real-world map space, from the image data using a trainable object localization Convolutional Neural Network (CNN). The CNN is trained to detect and localize the objects from image and radar pairs that include the image data and radar data for different driving scenes of a natural driving environment. The processor is further configured to provide a user-perceptible object detection result to a user of the motor vehicle.
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公开(公告)号：US20180082137A1
公开(公告)日：2018-03-22
申请号：US15689755
申请日：2017-08-29
Applicant: NEC Laboratories America, Inc.
Inventor： Iain Melvin , Eric Cosatto , Igor Durdanovic , Hans Peter Graf
CPC classification number: G01S13/931 , B60G2400/823 , B60Q9/008 , B60R1/00 , B60R2300/301 , B60R2300/8093 , B60W30/09 , B60W2420/42 , B60W2420/52 , G01S7/20 , G01S7/2955 , G01S7/417 , G01S13/867 , G01S17/936 , G01S2013/936 , G01S2013/9367 , G01S2013/9375 , G06K9/00805 , G06K9/46 , G06K9/6215 , G06K9/6232 , G06N3/0454 , G06N3/08 , G06N3/084
Abstract: A computer-implemented method and system are provided for driving assistance. The system includes an image capture device configured to capture image data relative to an outward view from a motor vehicle. The system further includes a processor configured to detect and localize objects, in a real-world map space, from the image data using a trainable object localization Convolutional Neural Network (CNN). The CNN is trained to detect and localize the objects from image and radar pairs that include the image data and radar data for different driving scenes of a natural driving environment. The processor is further configured to provide a user-perceptible object detection result to a user of the motor vehicle.
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Patent Agency Ranking
公开(公告)号：US20170293837A1
公开(公告)日：2017-10-12
申请号：US15478823
申请日：2017-04-04
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Iain Melvin , Hans Peter Graf
CPC classification number: G06K9/00805 , B60Q9/008 , G06K9/00201 , G06K9/46 , G06K9/4628 , G06K9/627 , G06N3/0454 , G06N3/08
Abstract: A computer-implemented method for training a deep neural network to recognize traffic scenes (TSs) from multi-modal sensors and knowledge data is presented. The computer-implemented method includes receiving data from the multi-modal sensors and the knowledge data and extracting feature maps from the multi-modal sensors and the knowledge data by using a traffic participant (TS) extractor to generate a first set of data, using a static objects extractor to generate a second set of data, and using an additional information extractor. The computer-implemented method further includes training the deep neural network, with training data, to recognize the TSs from a viewpoint of a vehicle.
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公开(公告)号：US20170293815A1
公开(公告)日：2017-10-12
申请号：US15478886
申请日：2017-04-04
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Iain Melvin , Hans Peter Graf
IPC: G06K9/00 , G06K9/66 , G06K9/46 , B60Q9/00 , G06N3/08 , B60R11/04 , B60R1/00 , G06K9/78 , G06N3/04
CPC classification number: G06K9/00805 , B60Q9/008 , G01S19/13 , G06K9/00201 , G06K9/46 , G06K9/4628 , G06K9/627 , G06N3/04 , G06N3/08
Abstract: A video device for predicting driving situations while a person drives a car is presented. The video device includes multi-modal sensors and knowledge data for extracting feature maps, a deep neural network trained with training data to recognize real-time traffic scenes (TSs) from a viewpoint of the car, and a user interface (UI) for displaying the real-time TSs. The real-time TSs are compared to predetermined TSs to predict the driving situations. The video device can be a video camera. The video camera can be mounted to a windshield of the car. Alternatively, the video camera can be incorporated into the dashboard or console area of the car. The video camera can calculate speed, velocity, type, and/or position information related to other cars within the real-time TS. The video camera can also include warning indicators, such as light emitting diodes (LEDs) that emit different colors for the different driving situations.
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28.发明申请公开(公告)号：US20170289409A1
公开(公告)日：2017-10-05
申请号：US15380014
申请日：2016-12-15
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Dongjin Song , Eric Cosatto
CPC classification number: G06K9/00718 , G06K9/4628 , G06K9/6271 , G06K9/66 , G06K2009/00738 , G06N3/04 , G06N3/0454 , G06N3/0481 , G06N3/084 , H04N5/21
Abstract: A computer-implemented method and system are provided for video-based anomaly detection. The method includes forming, by a processor, a Deep High-Order Convolutional Neural Network (DHOCNN)-based model having a one-class Support Vector Machine (SVM) as a loss layer of the DHOCNN-based model. An objective of the SVM is configured to perform the video-based anomaly detection. The method further includes generating, by the processor, one or more predictions of an impending anomaly based on the high-order deep learning based model applied to an input image. The method also includes initiating, by the processor, an action to a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.
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公开(公告)号：US09592828B2
公开(公告)日：2017-03-14
申请号：US15088494
申请日：2016-04-01
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto
CPC classification number: B60Q9/008 , B60Q5/006 , B60Q9/00 , B60W10/04 , B60W10/18 , B60W10/30 , B60W30/0953 , B60W30/0956 , B60W40/04 , B60W40/06 , B60W40/09 , B60W50/14 , B60W50/16 , B60W2040/0827 , B60W2050/143 , B60W2050/146 , B60W2420/42 , B60W2420/52 , B60W2540/22 , B60W2540/26 , B60W2540/30 , B60W2550/12 , B60W2550/20 , B60W2550/402 , B60W2710/18 , B60W2710/30 , B60W2720/106
Abstract: Systems and methods are disclosed to assist a driver with a dangerous condition by creating a graph representation where traffic participants and static elements are the vertices and the edges are relations between pairs of vertices; adding attributes to the vertices and edges of the graph based on information obtained on the driving vehicle, the traffic participants and additional information; creating a codebook of dangerous driving situations, each represented as graphs; performing subgraph matching between the graphs in the codebook and the graph representing a current driving situation to select a set of matching graphs from the codebook; determining a distance metric between each selected codebook graphs and the matching subgraph of the current driving situation; from codebook graphs with a low distance, determining potential dangers; and generating an alert if one or more of the codebook dangers are imminent.
Abstract translation: 公开了系统和方法，以通过创建图形表示来帮助驾驶员处于危险状态，其中交通参与者和静态元素是顶点，并且边缘是顶点对之间的关系; 根据驾驶车辆上获得的信息，交通参与者和附加信息，向图形的顶点和边缘添加属性; 制作危险驾驶状况的码本，每个代表图表; 执行代码本中的图表和表示当前驾驶状况的图形之间的子图匹配以从码本中选择一组匹配图; 确定每个所选码本图和当前驾驶状况的匹配子图之间的距离度量; 从距离较小的码本图，确定潜在的危险; 并且如果迫切需要一个或多个码本危险，则生成警报。
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30.发明申请System and Method for Detecting Objects Obstructing a Driver's View of a Road 有权用于检测物体的系统和方法阻碍驾驶员对道路的看法公开(公告)号：US20160054452A1
公开(公告)日：2016-02-25
申请号：US14830873
申请日：2015-08-20
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Hans Peter Graf
CPC classification number: G01S19/51 , B60K35/00 , B60K2350/1084 , B60K2350/962 , B60K2350/965 , G01C21/3629 , G01C21/3697 , G01S11/12 , G01S13/867 , G01S13/931 , G01S17/89 , G01S17/936 , G01S19/14 , G01S19/48 , G01S19/49
Abstract: A system and method for a motorized land vehicle that detects objects obstructing a driver's view of an active road, includes an inertial measurement unit-enabled global position system (GPS/IMU) subsystem for obtaining global position system (GPS) position and heading data of a land vehicle operated by the driver as the vehicle travels along a road, a street map subsystem for obtaining street map data of the GPS position of the vehicle using the GPS position and heading data as the vehicle travels along the road, and a three-dimensional (3D) object detector subsystem for detecting objects ahead of the vehicle and determining a 3D position and 3D size data of each of the detected objects ahead of the vehicle. The street map subsystem merges the street map data, the GPS position and heading data of the vehicle and the 3D position data and 3D size data of the detected objects, to create real-time two-dimensional (2D) top-view map representation of a traffic scene ahead of the vehicle. The street map subsystems finds active roads ahead of the vehicle in the traffic scene, and finds each active road segment of the active roads ahead of the vehicle that is obstructed by one of the detected objects. A driver alert subsystem notifies a driver of the vehicle of each of the active road segments that is obstructed by one of the detected objects.
Abstract translation: 一种用于检测妨碍驾驶员对主动道路的观察的物体的电动陆地车辆的系统和方法包括：具有惯性测量单元的全球定位系统（GPS / IMU）子系统，用于获得全球定位系统（GPS）的位置和航向数据 当车辆沿着道路行驶时由驾驶员操作的陆地车辆;街道地图子系统，用于当车辆沿着道路行进时，使用GPS位置和航向数据获取车辆的GPS位置的街道地图数据; （3D）物体检测器子系统，用于检测车辆前方的物体，并确定车辆前方的每个检测物体的3D位置和3D尺寸数据。 街道地图子系统将街道地图数据，车辆的GPS位置和航向数据以及检测到的物体的3D位置数据和3D尺寸数据进行合并，以创建实时二维（2D）顶视图地图 车辆前方的交通情况。 街道地图子系统在交通场景中找到车辆前方的活动道路，并且找到被检测到的对象之一阻挡的车辆之前的活动道路的每个活动路段。 驾驶员警报子系统通知被检测对象之一阻塞的每个活动道路段的车辆的驾驶员。
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