An image-processing apparatus, comprising: an image sensor configured to capture a sequence of color image frames; a memory configured to store a first neural network model and a second neural network model; and control circuitry configured to: generate, based on the first neural network model, a first foreground mask for an object-of-interest in a first color image frame of the sequence of color image frames; generate, based on the first neural network model, a second foreground mask for the object-of-interest in an upcoming color image frame that is non-adjacent to the first color image frame of the sequence of color image frames; determine a third foreground mask based on interpolation of the first foreground mask and the second foreground mask; update, by use of the second neural network model, the third foreground mask to a fourth foreground mask that represents a refined foreground mask of the third foreground mask, based on the determined third foreground mask and an intermediate color image frame that lies between the first color image frame and the upcoming color image frame; and segment the object-of-interest from at least the first color image frame by use of the first foreground mask, the upcoming color image frame by use of the second foreground mask, and the intermediate color image frame by use of the fourth foreground mask. an image sensor configured to capture a sequence of color image frames; a memory configured to store a first neural network model and a second neural network model; and control circuitry configured to: generate, based on the first neural network model, a first foreground mask for an object-of-interest in a first color image frame of the sequence of color image frames; generate, based on the first neural network model, a second foreground mask for the object-of-interest in an upcoming color image frame that is non-adjacent to the first color image frame of the sequence of color image frames; determine a third foreground mask based on interpolation of the first foreground mask and the second foreground mask; update, by use of the second neural network model, the third foreground mask to a fourth foreground mask that represents a refined foreground mask of the third foreground mask, based on the determined third foreground mask and an intermediate color image frame that lies between the first color image frame and the upcoming color image frame; and segment the object-of-interest from at least the first color image frame by use of the first foreground mask, the upcoming color image frame by use of the second foreground mask, and the intermediate color image frame by use of the fourth foreground mask. generate, based on the first neural network model, a first foreground mask for an object-of-interest in a first color image frame of the sequence of color image frames; generate, based on the first neural network model, a second foreground mask for the object-of-interest in an upcoming color image frame that is non-adjacent to the first color image frame of the sequence of color image frames; determine a third foreground mask based on interpolation of the first foreground mask and the second foreground mask; update, by use of the second neural network model, the third foreground mask to a fourth foreground mask that represents a refined foreground mask of the third foreground mask, based on the determined third foreground mask and an intermediate color image frame that lies between the first color image frame and the upcoming color image frame; and segment the object-of-interest from at least the first color image frame by use of the first foreground mask, the upcoming color image frame by use of the second foreground mask, and the intermediate color image frame by use of the fourth foreground mask.