Samsung Electronics Co., Ltd.

Beijing Sankuai Online Technology Co., Ltd

Deheng Qian

Jingtao Xu

Jae-Joon Han

Byungin Yoo

Dongchun Ren

Chang Kyu Choi

Hui Zhang

Biao Wang

Qiang Wang

Youngsung Kim

Changkyu Choi

Huaxia Xia

Tianchu Guo

Mingyu Fan

Chao Zhang

Keun Woo Park

Jia Qi Li

Hyunsurk Eric Ryu

Hao Feng

Feng Shi

Dongqing Zou

Sheng Fu

Yaozu An

Guangwei Wang

Weiheng Liu

Yanliang Zhu

Shuguang Ding

Xin Li

Lin Ma

Bing Yu

Haitao Wang

Tao Hong

Jingu Heo

Weiming Li

Zhihua Liu

He Zheng

Mingcai Zhou

Dongkyung Nam

Zairan Wang

Yinian Mao

Changwoo Shin

Qin Han

Xiaoyang Guo

JinMan Park

Shuwang Xu

Xu Du

Mingcai Zho

Vehicle control is provided, including: obtaining vehicle information of a target vehicle and environmental information of a reference environment in which the target vehicle is located; obtaining a target matrix based on the vehicle information and the environmental information; splitting the target matrix to obtain a plurality of sub-matrices; and obtaining target driving control information of the target vehicle based on matrix elements in the sub-matrices and driving control information of a surrounding vehicle of the target vehicle.

Vehicle control

An obstacle trajectory prediction method and apparatus are provided. In some embodiments, a global interaction feature under joint action of a vehicle and obstacles is determined according to historical status information and current status information of the vehicle, historical status information and current status information of the obstacles, and a future motion trajectory planned by the vehicle; an individual interaction feature of a to-be-predicted obstacle is determined according to the global interaction feature and current status information of the to-be-predicted obstacle; and a future motion trajectory of the to-be-predicted obstacle is predicted through the individual interaction feature and information about an environment around the vehicle.

Obstacle trajectory prediction method and apparatus

A trajectory prediction method and apparatus, a storage medium, and an electronic device are provided. In embodiments of this disclosure, according to a historical trajectory of a designated target and a historical trajectory of each obstacle, a historical interaction feature between the designated target and each obstacle is determined, and a motion trajectory of the designated target is predicted to obtain an initial predicted trajectory. A future interaction feature between each obstacle and the designated target is then determined according to the initial predicted trajectory and a planned trajectory of each obstacle. According to the future interaction feature, a final predicted trajectory of the designated target is obtained.

Trajectory prediction method and apparatus, storage medium, and electronic device

An unmanned device acquires sensing data of surrounding obstacles; determines, for each obstacle, at least one predicted track of the obstacle in a future period of time based on the sensing data; determines, for each moment in the future period of time and according to the predicted track corresponding to the obstacle, a collision probability that a collision with the obstacle occurs at each position in a target region at the moment; and determines a global collision probability that the collision with the obstacle occurs in the entire target region at the moment. According to the global collision probability corresponding to each obstacle at each moment, the unmanned device controls the unmanned device in the future period of time.

Unmanned device control based on future collision risk

Various embodiments a method and apparatus for unmanned driving behavior decision-making and model training, and an electronic device. The method includes: acquiring sample data, wherein the sample data includes a sample image; extracting a sample feature vector corresponding to the sample data, wherein a feature vector of the sample image is extracted by manifold dimension reduction; and based on the sample feature vector, training by semi-supervised learning to obtain a target decision-making model, wherein the target decision-making model is used for decision-making classification.

Unmanned driving behavior decision-making and model training

A processor implemented method of processing a facial expression image, the method includes controlling a camera to capture a first facial expression image and a second facial expression image, acquiring a first expression feature of the first facial expression image, acquiring a second expression feature of the second facial expression image, generating a new expression feature dependent on differences between the acquired first expression feature and the acquired second expression feature, and adjusting a target facial expression image based on the new expression feature.

Facial expression image processing method and apparatus

The present invention provides a method and device for adjusting resolution of a Head-Mounted Display (HMD) apparatus. Wherein, the method comprises the steps of: determining saliency information of display contents in multimedia information; adjusting, according to the saliency information, resolution corresponding to each display content in the multimedia information; and, displaying the resolution-adjusted multimedia information. In the present invention, by determining saliency information of display contents in multimedia information, the resolution corresponding to the display contents is adjusted, which enable the resolution of the display contents to be matched with the saliency of the display contents, and accordingly, the resolution-adjusted multimedia information is displayed without the need of displaying the complete multimedia information in a higher resolution and a higher frame rate. In this way, computation amount for a device is greatly reduced, so that requirement for device hardware is reduced and device cost is reduced, and displaying efficiency is improved whilst at the same time ensuring good displaying effect.

Method and device for adjusting resolution of HMD apparatus

A method of detecting a target includes generating an image pyramid based on an image on which a detection is to be performed; classifying candidate areas in the image pyramid using a cascade neural network; and determining a target area corresponding to a target included in the image based on the plurality of candidate areas, wherein the cascade neural network includes a plurality of neural networks, and at least one neural network among the neural networks includes parallel sub-neural networks.

Target detection method and apparatus

A processor-implemented method includes acquiring, by a processor, a first undirected graph and a second undirected graph, generating, by the processor, a first lattice for the first undirected graph and a second lattice for the second undirected graph; matching, by the processor, the first lattice and the second lattice based on a first global structure of the first lattice and a second global structure of the second lattice, the first global structure corresponding to nodes of the first undirected graph and the second global structure corresponding to nodes of the second undirected graph, and processing the first undirected graph and the second undirected graph based on a result of the matching of the first lattice and the second lattice.

Method and apparatus for processing a plurality of undirected graphs

An image processing apparatus, includes an image classifier configured to determine whether an input image is a low-quality image or a high-quality image; and an image evaluator configured to determine a first predetermined number of clearest images from a plurality of low-quality images determined by the image classifier.

Image processing method and apparatus

A processor implemented method of processing a facial expression image, the method includes acquiring an expression feature of each of at least two reference facial expression images; generating a new expression feature based on an interpolation value of the expression feature; and adjusting a target facial expression image based on the new expression feature and creating a new facial expression image.

A method of detecting a target includes determining a quality type of a target image captured using a camera, determining a convolutional neural network of a quality type corresponding to the quality type of the target image in a database comprising convolutional neural networks, determining a detection value of the target image based on the convolutional neural network of the corresponding quality type, and determining whether a target in the target image is a true target based on the detection value of the target image.

Method and apparatus detecting a target

The disclosure provides an object detection method and apparatus based on a Dynamic Vision Sensor (DVS). The method includes the following operations of: acquiring a plurality of image frames by a DVS; and, detecting the image frames by a recurrent coherent network to acquire a candidate box for objects to be detected, wherein the recurrent coherent network comprising a frame detection network model and a candidate graph model. By using a new recurrent coherent detection network, a bounding box for an object to be detected is fast detected from the data acquired by a DVS. The detection speed is improved greatly while ensuring the detection accuracy.

Object detection method and apparatus based on dynamic vision sensor

An image processing apparatus includes a calculator configured to calculate a respective position offset for each of a plurality of candidate areas in a second frame based on a position of a basis image in a first frame and a determiner configured to determine a final selected area that includes a target in the second frame based on a respective weight allocated to each of the plurality of candidate areas and the calculated respective position offset.

Image processing apparatus and method

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Beijing, China

Deheng Qian