Samsung Electronics Co., Ltd.

Imperial Innovations Limited

Yonsei University

Korea University of Technology and Education Industry-university Cooperation Foundation

Imperial College London

Korea University of Technology and Education Industry-university Corporation Foundation

Byung In Yoo

Chang Kyu Choi

Youngjun Kwak

Jae Joon Han

Changbeom Park

Hana Lee

Yong-Il Lee

Jaejoon Han

Jiho Choi

Solae Lee

Seohyung Lee

Dongwook Lee

Youngsung Kim

Qiang Wang

Du Sik Park

Changyong Son

Seungin Park

Sangil Jung

Hyo Sun Hwang

Seung Ju Han

Jingtao Xu

Yong Beom Lee

Hyunjeong Lee

Sung Joo Suh

Young Chul Cho

Hui Zhang

Seon Min Rhee

Minsu Ko

Seungwook Kim

Seungju Han

Hyong Euk Lee

Jinwoo Son

Tae Kyun Kim

Yi Zhou

Kwon Ju Yi

Juwoan Yoo

Heewon Kim

Jinwoo Kim

Jihye Kim

Kyung Hwan Kim

Jungbae Kim

Sujin Lee

Sangwon Lee

Changkyu Choi

Jung Bae Kim

Nahyup Kang

Won Jun Hwang

Changkyo Lee

Sungheon Park

Jiaqian Yu

HyunJeong Lee

Chang Kyo Lee

Yiwei Chen

Ju Hwan Song

Gyeong Ja Jang

Hee Seob Ryu

Hyung Min Yoon

Hyeongwook Yang

Pingjun Li

Won Jun Kim

Yunhao Zhang

Danhang Tang

Alykhan Tejani

Hyung Jin Chang

Sung Kwang Cho

Jae Seok Choi

Kinam Kwon

Tianchu Guo

Nam Joon Kim

Huxing Sun

Siyang Pan

Feng Qiu

Eunhee Kang

Xiabing Liu

Yeonkyung Lee

Yongchao Liu

Seon Joo Kim

Hangkai Tan

Chang Mug Lee

Jonghoon Yoon

Sungyoul Park

DongYoung Kim

Hyeongseok Son

Oh Young Kwon

Hyo Seok Seo

Chyangbeom Park

Young Jun Kwak

Deheng Qian

An image sensor includes: a motion detection circuit configured to detect a motion in image frames; and a micro control unit (MCU) configured to adjust at least a portion of a target frame among the image frames based on whether the motion is detected, and detect whether a target object is present based on the adjusted portion of the target frame.

Image sensor and operating method of the image sensor

A method with object tracking includes: determining a first target tracking state by tracking a target from a first image frame with a first field of view (FoV); determining a second FoV based on the first FoV and the first target tracking state; and generating a second target tracking result by tracking a target from a second image frame with the second FoV.

Method and apparatus with object tracking using dynamic field of view

A method of generating a hyperlapse video includes: comparing a first reference point of a first image and a corresponding second reference point of a second image; based on the comparing, displaying a first user interface for matching the first reference point and second reference point; and determining whether to perform automatic shooting for the hyperlapse video based on whether the first reference point and the second reference point match.

Method and apparatus with hyperlapse video generation

An object classification method and apparatus are disclosed. The object classification method includes receiving an input image, storing first feature data extracted by a first feature extraction layer of a neural network configured to extract features of the input image, receiving second feature data from a second feature extraction layer which is an upper layer of the first feature extraction layer, generating merged feature data by merging the first feature data and the second feature data, and classifying an object in the input image based on the merged feature data.

Method and apparatus with object classification

A method with self-attention includes: obtaining a three-dimensional (3D) feature map; generating 3D query data and 3D key data by performing a convolution operation based on the 3D feature map; generating two-dimensional (2D) vertical data based on a vertical projection of the 3D query data and the 3D key data; generating 2D horizontal data based on a horizontal projection of the 3D query data and the 3D key data; determining an intermediate attention result through a multiplication based on the 2D vertical data and the 2D horizontal data; and determining a final attention result through a multiplication based on the intermediate attention result and the 3D feature map.

Method and apparatus with self-attention-based image recognition

A processor-implemented method with target object tracking includes: setting a search area for a target object included in an input image based on a position of a first target box in a template image; selecting a network path from a plurality of network paths of a neural network model according to a resizing ratio of a size of another image that is input to the neural network model to a size of the search area; and tracking the target object by estimating a position of a second target box corresponding to the target object in the input image according to the selected network path.

Method and apparatus with target object tracking

A method and apparatus with object tracking is provided. The method includes generating a mixed filter by fusing a short-term filter with a long-term filter; and performing object tracking on a current frame image based on the mixed filter. The short-term filter is dependent on a prediction of the current frame image in a video sequence, and the long-term filter is a previously generated long-term filter or is generated by optimizing the previously generated long-term filter based on an object template feature pool.

Method and apparatus with object tracking

An apparatus and a method of controlling a camera is provided. The apparatus includes a receiver configured to receive an image from an image sensor and a processor configured to detect a target object from the image, perform a first zoom adjustment operation of the camera including the image sensor based on whether the target object is changed and perform a second zoom adjustment of the camera based on a center point of the target object and a center point of the camera.

Apparatus and method of controlling camera with auto-zooming

A processor-implemented method with object tracking includes: performing, using a first template, forward object tracking on first image frames in a first sequence group; determining a template candidate of a second template for second image frames in a second sequence group; performing backward object tracking on the first image frames using the template candidate; determining a confidence of the template candidate using a result of comparing a first tracking result determined by the forward object tracking performed on the first image frames and a second tracking result determined by the backward object tracking performed on the first image frames; determining the second template based on the confidence of the template candidate; and performing forward object tracking on the second image frames using the second template.

A processor-implemented method includes: determining a probability that a pixel of an input image belongs to each of a plurality of preset categories; and determining a category of the pixel to be a category corresponding to either one or both of a plurality of category areas and a category determined based on the probability that the pixel belongs to each of the preset categories, based on a result of comparing, to a preset threshold value, a probability that the pixel belongs to the category corresponding to the category areas.

Method and apparatus with optimization and prediction for image segmentation

A processor-implemented method with target tracking includes: generating a first target tracking result based on a search region of a current frame image; determining a scale feature of the first target tracking result; predicting a scale of a target in the search region based on the scale feature of the first target tracking result; and generating a second target tracking result by adjusting the first target tracking result based on a scale predicting result.

Method and apparatus with target tracking

A method and apparatus for object tracking are provided, where the object tracking method includes determining box information of candidate boxes in a current image frame and similarity scores of the candidate boxes based on including a search region of the current image frame with a template image corresponding to a target object, adjusting the similarity scores of the candidate boxes using a distractor map including distractor information of a previous image frame, determining a target box corresponding to the target object and a distractor box corresponding to a distractor of the target object from the candidate boxes based on the adjusted similarity scores, and updating the distractor map based on distractor information of the current image frame according to the distractor box.

A processor-implemented method with liveness detection includes: receiving a plurality of phase images of different phases; generating a plurality of preprocessed phase images by performing preprocessing, including edge enhancement processing, on the plurality of phase images of different phases; generating a plurality of differential images based on the preprocessed phase images; generating a plurality of low-resolution differential images having lower resolutions than the differential images, based on the differential images; generating a minimum map image based on the low-resolution differential images; and performing a liveness detection on an object in the phase images based on the minimum map image.

Method and apparatus with liveness detection

A processor-implemented method includes generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image.

Method and apparatus with liveness test and/or biometric authentication

A target object detection method and apparatus are provided. The target object detection method and apparatus are applicable to fields such as artificial intelligence, object tracking, object detection, and image processing. An object is detected from a frame image of a video including a plurality of frame images based on a target template set including one or more target templates.

Video object detection and tracking method and apparatus

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Seoul, South Korea

Byung In Yoo