A method for long-term animal tracking, comprising: receiving, by a processor, video data representing continuous observation of an animal; and executing, by the processor, a neural network architecture configured to: receive an input video frame extracted from the video data; characterised in that the neural network architecture is further configured to: generate an ellipse description of at least one animal based upon the input video frame, the ellipse description being defined by predetermined ellipse parameters; and provide data including values characterizing the predetermined ellipse parameters for the at least one animal; and wherein the neural network architecture is selected from: a) an encoder-decoder segmentation network configured to: predict a foreground-background segmented image from an input video frame; predict, on a pixel-wise basis, whether an animal is present in the input video frame based upon the segmented image; output a segmentation mask based upon the pixel-wise prediction; and fit the portions of the segmentation mask where the animal is predicted to be present to an ellipse to determine the values characterizing the predetermined ellipse parameters; or b) a binned classification network configured to predict a heat map of a most probable value for each ellipse parameter of the ellipse description; or c) a regression network configured to extract features from an input video frame and directly predict the values characterizing each of the ellipse parameters. receiving, by a processor, video data representing continuous observation of an animal; and executing, by the processor, a neural network architecture configured to: receive an input video frame extracted from the video data; characterised in that the neural network architecture is further configured to: generate an ellipse description of at least one animal based upon the input video frame, the ellipse description being defined by predetermined ellipse parameters; and provide data including values characterizing the predetermined ellipse parameters for the at least one animal; receive an input video frame extracted from the video data; characterised in that the neural network architecture is further configured to: generate an ellipse description of at least one animal based upon the input video frame, the ellipse description being defined by predetermined ellipse parameters; and provide data including values characterizing the predetermined ellipse parameters for the at least one animal; generate an ellipse description of at least one animal based upon the input video frame, the ellipse description being defined by predetermined ellipse parameters; and provide data including values characterizing the predetermined ellipse parameters for the at least one animal; and wherein the neural network architecture is selected from: a) an encoder-decoder segmentation network configured to: predict a foreground-background segmented image from an input video frame; predict, on a pixel-wise basis, whether an animal is present in the input video frame based upon the segmented image; output a segmentation mask based upon the pixel-wise prediction; and fit the portions of the segmentation mask where the animal is predicted to be present to an ellipse to determine the values characterizing the predetermined ellipse parameters; or b) a binned classification network configured to predict a heat map of a most probable value for each ellipse parameter of the ellipse description; or c) a regression network configured to extract features from an input video frame and directly predict the values characterizing each of the ellipse parameters. a) an encoder-decoder segmentation network configured to: predict a foreground-background segmented image from an input video frame; predict, on a pixel-wise basis, whether an animal is present in the input video frame based upon the segmented image; output a segmentation mask based upon the pixel-wise prediction; and fit the portions of the segmentation mask where the animal is predicted to be present to an ellipse to determine the values characterizing the predetermined ellipse parameters; or predict a foreground-background segmented image from an input video frame; predict, on a pixel-wise basis, whether an animal is present in the input video frame based upon the segmented image; output a segmentation mask based upon the pixel-wise prediction; and fit the portions of the segmentation mask where the animal is predicted to be present to an ellipse to determine the values characterizing the predetermined ellipse parameters; or b) a binned classification network configured to predict a heat map of a most probable value for each ellipse parameter of the ellipse description; or c) a regression network configured to extract features from an input video frame and directly predict the values characterizing each of the ellipse parameters.