A method for determining position and attitude, implemented by a smart device, the method comprising: acquiring, by tracking first feature points extracted from a marked image, position and attitude parameters of a first image captured by a camera relative to the marked image, the first feature points being extracted from the marked image; acquiring a number of the first feature points tracked in the first image; determining, in response to the number of the first feature points failing to reach a preset number, that the first image fails to meet a feature point tracking condition, extracting second feature points from the first image, the second feature points being different from the first feature points; and acquiring, by tracking the first feature points and the second feature points, position and attitude parameters of a second image captured by the camera relative to the marked image, and determining a position and an attitude of the camera according to the position and attitude parameters, the second image being an image captured by the camera after the first image, wherein the method further comprises: before acquiring, by tracking the first feature points extracted from the marked image, the position and attitude parameters of the first image captured by the camera relative to the marked image, dividing the marked image into a plurality of grid areas of a same size; acquiring a weight of each feature point extracted from the marked image, the weight of the feature point being used for representing a gradient of the feature point; and extracting, as the first feature points, a feature point with the highest weight from each of the divided grid areas, until the first feature points are extracted from all of the grid areas in the marked image or until a number of the first feature points extracted from the marked image reaches a preset number. acquiring, by tracking first feature points extracted from a marked image, position and attitude parameters of a first image captured by a camera relative to the marked image, the first feature points being extracted from the marked image; acquiring a number of the first feature points tracked in the first image; determining, in response to the number of the first feature points failing to reach a preset number, that the first image fails to meet a feature point tracking condition, extracting second feature points from the first image, the second feature points being different from the first feature points; and acquiring, by tracking the first feature points and the second feature points, position and attitude parameters of a second image captured by the camera relative to the marked image, and determining a position and an attitude of the camera according to the position and attitude parameters, the second image being an image captured by the camera after the first image, wherein the method further comprises: before acquiring, by tracking the first feature points extracted from the marked image, the position and attitude parameters of the first image captured by the camera relative to the marked image, dividing the marked image into a plurality of grid areas of a same size; acquiring a weight of each feature point extracted from the marked image, the weight of the feature point being used for representing a gradient of the feature point; and extracting, as the first feature points, a feature point with the highest weight from each of the divided grid areas, until the first feature points are extracted from all of the grid areas in the marked image or until a number of the first feature points extracted from the marked image reaches a preset number. before acquiring, by tracking the first feature points extracted from the marked image, the position and attitude parameters of the first image captured by the camera relative to the marked image, dividing the marked image into a plurality of grid areas of a same size; acquiring a weight of each feature point extracted from the marked image, the weight of the feature point being used for representing a gradient of the feature point; and extracting, as the first feature points, a feature point with the highest weight from each of the divided grid areas, until the first feature points are extracted from all of the grid areas in the marked image or until a number of the first feature points extracted from the marked image reaches a preset number.