Qualcomm Incorporated

Duck Hoon Kim

Young-Ki Baik

Hee-Seok Lee

Hyun-Mook Cho

Seungwoo Yoo

Jeong-Kyun Lee

ChaeSeong Lim

Taesu Kim

Kang Min Kim

Minho Jin

Kyuwoong Hwang

Seok-Soo Hong

Te-Won Lee

Sungwoong Kim

Xinzhou Wu

Jayakrishnan Unnikrishnan

Ravi Teja Sukhavasi

Sungrack Yun

Hyung-Il Koo

Kisun You

Avdhut Joshi

Yongwoo Cho

Minsub Lee

Yoga Y Nadaraajan

Heesoo Myeong

Amir Salimi

Gautam Sachdeva

Shivam Agarwal

Sree Sesha Aravind Vadrevu

Disclosed are techniques for estimating a 3D bounding box (3DBB) from a 2D bounding box (2DBB). Conventional techniques to estimate 3DBB from 2DBB rely upon classifying target vehicles within the 2DBB. When the target vehicle is misclassified, the projected bounding box from the estimated 3DBB is inaccurate. To address such issues, it is proposed to estimate the 3DBB without relying upon classifying the target vehicle.

Bounding box estimation and object detection

Bounding box estimation and lane vehicle association

Disclosed are techniques for determining a motion state of a target object. In an aspect, an on-board computer of an ego vehicle detects the target object in one or more images, determines one or more first attributes of the target object based on measurements of the one or more images, determines one or more second attributes of the target object based on measurements of a map of a roadway on which the target object is travelling, and determines the motion state of the target object based on the one or more first attributes and the one or more second attributes of the target object.

Determining a motion state of a target object

Disclosed are techniques for lane estimation. In aspects, a method includes receiving a plurality of camera frames captured by a camera sensor of a vehicle, assigning a first subset of the plurality of camera frames to a deep learning (DL) detector and a second subset of the plurality of camera frames to a computer vision (CV) detector based on availability of the DL and CV detectors, identifying a first set of lane boundary lines in a first camera frame processed by the DL detector, identifying a second set of lane boundary lines in a second camera frame processed by the CV detector, generating first and second sets of lane models based on the first and second sets of lane boundary lines, and updating a set of previously identified lane models based on the first set of lane models and/or the second set of lane models.

Hybrid lane estimation using both deep learning and computer vision

Disclosed are techniques for performing lane instance recognition. Lane instances are difficult to recognize since they are long and elongated, and they also look different from view to view. An approach is proposed in which local mask segmentation lane estimation and global control points lane estimation are combined.

Lane marker detection and lane instance recognition

Disclosed are techniques for estimating multiple lane boundaries through simultaneous detection of lane markers (LM) and raised pavement markers (RPM). Conventional techniques for lane marker detection (LMD) comprises extracting and clustering line segments from a camera image, fitting the clustered lines to a geometric model, and selecting the multiple lanes using heuristic approaches. Unfortunately, in the conventional technique, an error from each step is sequentially propagated to the next step. Also there are no techniques to estimate both LMs and RPMs. A technique to simultaneously detect in real time both LM and RPMs is proposed. This enables optimal estimation of multiple lane boundaries.

Real-time simultaneous detection of lane marker and raised pavement marker for optimal estimation of multiple lane boundaries

An electronic device is described. The electronic device includes a memory and a processor in communication with the memory. The processor is configured to receive an image. The processor is also configured to extract a lane response map from the image. The processor is further configured to estimate one or more lane markers based on the lane response map.

Systems and methods for lane-marker detection

A method for capturing an image of a traffic sign by an image sensor is disclosed. The method may include capturing, by the image sensor, at least one image including the traffic sign, wherein the image sensor is mounted in a vehicle. The method may include detecting, by a processor, the traffic sign in the at least one image. The method may also include in response to detecting the traffic sign, determining, by the processor, at least one direction of the image sensor based on the at least one image and motion of the vehicle. In addition, the method may include adjusting, by the processor, the image sensor to the at least one direction.

Method and device for capturing image of traffic sign

A method, a computer-readable medium, and an apparatus for object detection are provided. The apparatus may determine a regression vector using a neural network based on an input image that contains an object. The object may have a planar surface with a known shape. The apparatus may derive a transform matrix based on the regression vector. The apparatus may identify a precise boundary of the object based on the transform matrix. The precise boundary of the object may include a plurality of vertices of the object. To identify the boundary of the object, the apparatus may apply the transform matrix to a determined shape of the object.

Simultaneous object detection and rigid transform estimation using neural network

A method, performed by an electronic device, for linking a thumbnail of an image and at least one web page is disclosed. In this method, the image including at least one text region may be accessed in a storage unit. At least one text region may be detected in the image and at least one character string in the at least one text region may be recognized. Further, the method may include selecting the at least one web page from the plurality of web pages and linking the thumbnail of the image and the at least one web page.

Linking thumbnail of image to web page

A method for controlling an electronic device in response to speech spoken by a user is disclosed. The method may include receiving an input sound by a sound sensor. The method may also detect the speech spoken by the user in the input sound, determine first characteristics of a first frequency range and second characteristics of a second frequency range of the speech in response to detecting the speech in the input sound, and determine whether a direction of departure of the speech spoken by the user is toward the electronic device based on the first and second characteristics.

Controlling electronic device based on direction of speech

A method, which is performed by an electronic device, of automatically activating a flash for an image sensor of the electronic device is disclosed. The method may include receiving a first image including at least one text region and determining feature data characterizing the at least one text region in the first image. The method may also identify at least one candidate specular reflection region in the first image. Based on the feature data and the at least one candidate specular reflection region, the flash may be activated. Upon activating the flash, a second image including the at least one text region may be captured.

Activating flash for capturing images with text

A method, performed in a control device, for adjusting a display device in communication with the control device is disclosed. The method may include receiving an image of a scene including the display device from an image sensor configured with a field of view overlapping at least a portion of view of a user. A display condition of the display device may be detected from the image. The control device may determine context information associated with the user from the image. Based on the display condition and the context information associated with the user, an output display condition of the display device may be determined. A control signal for adjusting the display device may then be generated based on the output display condition.

Growing community of inventors

Seoul, South Korea

Duck Hoon Kim