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 . <EOS>