Samsung Electronics Co., Ltd.

Jiaqian Yu

Jingtao Xu

Qiang Wang

Byung In Yoo

Yiwei Chen

Jaejoon Han

Changbeom Park

Chang Kyu Choi

HyunJeong Lee

Jae Joon Han

Youngjun Kwak

Sungun Park

Hana Lee

Hyunjeong Lee

Juwoan Yoo

Tushar Balasaheb Sandhan

Hyeongwook Yang

Siyang Pan

Hangkai Tan

Jihye Kim

Seohyung Lee

Feng Qiu

Chyangbeom Park

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 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

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 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

Provided is a liveness verification method and device. A liveness verification device acquires a first image and a second image, and select one or more liveness models based on respective analyses of the first image and the second image, including analyses based on an object part being detected in the first image and/or the second image, and to verify, using the selected one or more liveness models, a liveness of the object based on the first image and/or the second image. The first image may be a color image and the second image may be an Infrared image.

Method and apparatus with liveness verification

Disclosed is a target tracking method and apparatus. The target tracking apparatus includes a processor configured to obtain a first depth feature from a target region image and obtain a second depth feature from a search region image, obtain a global response diagram between the first depth feature and the second depth feature, acquire temporary bounding box information based on the global response diagram, updated the second depth feature based on the temporary bounding box information, obtain local feature blocks based on the first depth feature, obtain a local response diagram based on the local feature blocks and the updated second depth feature, and determine output bounding box information based on the local response diagram.

Method and apparatus for tracking target

A depth estimation method and apparatus are provided. The depth estimation method includes obtaining an image from an image sensor comprising upper pixels, each comprising N sub-pixels, obtaining N sub-images respectively corresponding to the N sub-pixels from the image, obtaining a viewpoint difference between the N sub-images using a first neural network, and obtaining a depth map of the image based on the viewpoint difference using a second neural network.

Depth estimation method and apparatus

A processor-implemented liveness detection method includes: obtaining an initial image using a dual pixel sensor; obtaining a left image and a right image from the initial image; and detecting liveness of an object included in the initial image using the left image and the right image.

Method and apparatus with liveness detection and object recognition

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Beijing, China

Jiaqian Yu