Abstract: 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.

Abstract: An object tracking apparatus is provided. The object tracking apparatus includes a processor configured to detect, from a first image frame, an amodal region including a first visible region in which a target object is visible and an occlusion region in which the target object is occluded, determine, based on the detected amodal region of the first image frame, that at least a partial region of a second image frame is a search region of the second image frame, the second image frame being temporally adjacent to the first image frame, and track the target object in the second image frame based on the determined search region.

Abstract: A target tracking method and apparatus is provided. The target tracking apparatus includes a memory configured to store a neural network, and a processor configured to extract feature information of each of a target included in a target region in a first input image, a background included in the target region, and a searching region in a second input image, using the neural network, obtain similarity information of the target and the searching region and similarity information of the background and the searching region based on the extracted feature information, obtain a score matrix including activated feature values based on the obtained similarity information, and estimate a position of the target in the searching region from the score matrix.

Abstract: An integrated circuit includes a plurality of cells in a series of rows, wherein a first cell of the plurality of cells includes a plurality of logic circuits, each logic circuit of the plurality of logic circuits configured to independently generate an output bit signal according to input bit signals, a first input pin group including at least one input pin commonly connected to the plurality of logic circuits, a second input pin group including at least one input pin commonly connected to two or more logic circuits among the plurality of logic circuits, and a third input pin group including at least one input pin respectively connected exclusively to at least one of the plurality of logic circuits.

Abstract: Disclosed is a method and device for processing data, and the method includes generating a target augmentation task sequence by processing the target data with a trained first model that performs inference on the target data to generate the target data augmentation task sequence, generate augmented target data by performing data augmentation on the target data according to the target augmentation task sequence, and obtaining a prediction result corresponding to the target data by inputting the augmented target data to a trained second model and performing a corresponding processing on the augmented target data by the trained second model.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A processor-implemented method with object tracking includes: determining an initial template image based on an input bounding box and an input image; generating an initial feature map by extracting features from the initial template image; generating a transformed feature map by performing feature transformation adapted to objectness on the initial feature map; generating an objectness probability map and a bounding box map indicating bounding box information corresponding to each coordinate of the objectness probability map by performing objectness-based bounding box regression analysis on the transformed feature map; and determining a refined bounding box based on the objectness probability map and the bounding box map.

Abstract: A processor-implemented object tracking method includes: setting a suppressed region in a template image based on a shape of a target box of the template image; refining a template feature map of the template image by suppressing an influence of feature data corresponding to the suppressed region in the template feature map; and tracking an object by determining a bounding box corresponding to the target box in a search image based on the refined template feature map.

Abstract: 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.

Abstract: 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.

Abstract: An apparatus and method with tracking a target are provided. A method includes determining whether data augmentation is to be used to augment a target tracking process, based on determining that data augmentation is to be used, performing the target tracking process based on an augmented image area obtained by the data augmentation on an image area, and outputting a tracking result generated by the target tracking process.

Abstract: 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.

Abstract: A target tracking method and apparatus is provided. The target tracking apparatus includes a memory configured to store a neural network, and a processor configured to extract feature information of each of a target included in a target region in a first input image, a background included in the target region, and a searching region in a second input image, using the neural network, obtain similarity information of the target and the searching region and similarity information of the background and the searching region based on the extracted feature information, obtain a score matrix including activated feature values based on the obtained similarity information, and estimate a position of the target in the searching region from the score matrix.

Abstract: A target tracking method and apparatus is provided. The target tracking apparatus includes a memory configured to store a neural network, and a processor configured to extract feature information of each of a target included in a target region in a first input image, a background included in the target region, and a searching region in a second input image, using the neural network, obtain similarity information of the target and the searching region and similarity information of the background and the searching region based on the extracted feature information, obtain a score matrix including activated feature values based on the obtained similarity information, and estimate a position of the target in the searching region from the score matrix.

Abstract: A processor-implemented method with object tracking includes: determining an initial template image based on an input bounding box and an input image; generating an initial feature map by extracting features from the initial template image; generating a transformed feature map by performing feature transformation adapted to objectness on the initial feature map; generating an objectness probability map and a bounding box map indicating bounding box information corresponding to each coordinate of the objectness probability map by performing objectness-based bounding box regression analysis on the transformed feature map; and determining a refined bounding box based on the objectness probability map and the bounding box map.

Abstract: Disclosed are target tracking methods and apparatuses. The target tracking apparatus performs target tracking on an input image obtained in a first time period within a single time frame, using a light neural network in a second time period of the single time frame. The target tracking apparatus may perform target tracking on input images generated within the same time frame.

Abstract: A processor-implemented object tracking method includes: setting a suppressed region in a template image based on a shape of a target box of the template image; refining a template feature map of the template image by suppressing an influence of feature data corresponding to the suppressed region in the template feature map; and tracking an object by determining a bounding box corresponding to the target box in a search image based on the refined template feature map.

Abstract: A scene flow estimation method and apparatus are provided. The scene flow estimation method includes receiving a first feature pyramid and a second feature pyramid by encoding a first frame and a second frame of an input image through the same encoder, extracting a depth feature based on the first feature pyramid, extracting a motion feature based on the first feature pyramid and the second feature pyramid, generating an overall feature based on the depth feature and the motion feature, and estimating a scene flow based on the overall feature.