Method and apparatus with adaptive object tracking

An apparatus, the apparatus comprising:

• a processor configured to:
• estimate a dynamic characteristic of an object in an input image based on frames of the input image, wherein the dynamic characteristic of the object includes at least one of a change of size, a change of shape, or a change of location of the object;
• determine a size of a crop region for a current frame of the input image based on the dynamic characteristic of the object;
• generate a cropped image by cropping the current frame based on the size of the crop region;
• and
• generate a result of tracking the object for the current frame using the cropped image.

An apparatus, the apparatus comprising:

• a processor configured to:
• estimate a dynamic characteristic of an object in an input image based on frames of the input image;
• determine a size of a crop region for a current frame of the input image based on the dynamic characteristic of the object;
• generate a cropped image by cropping the current frame based on the size of the crop region;
• select a meural network model corresponding to the size of the crop region from among neural network models configured to track the object;
• and
• generate a result of tracking the object for the current frame using the cropped image and the selected neural network model,
• wherein the neural network models comprise a first neural network model for a first size of the crop region and a second neural network model for a second size of the crop region.

AR-A.device, comprising:

• a camera configured to generate an input image based on sensed visual information;
• and
• a processor configured to:
• estimate a dynamic characteristic of an object in the input image based on frames of the input image;
• determine a size of a crop region for a current frame of the input image based on the dynamic characteristic of the object;
• generate a cropped image by cropping the current frame based on the size of the crop region;
• select a neural network model corresponding to the size of the crop region from among neural network models configured to track the object, and
• generate a result of tracking the object for the current frame using the cropped image and the selected neural network model,
• wherein the neural network models share at least one weight with each other.