A method for training a model, performed by a computing device, the method comprising: acquiring a template image and a test image for the training, the template image and the test image each comprising a tracked object, the test image comprising a tracking label of the tracked object, the tracking label being used for indicating a marking position of the tracked object in the test image; invoking a first object recognition model to extract features of the tracked object in the template image to obtain a first reference response, and invoking a second object recognition model to extract the features of the tracked object in the template image to obtain a second reference response, wherein each of the first object recognition model and the second object recognition model is a convolutional neural network model; invoking the first object recognition model to extract features of the tracked object in the test image to obtain a first test response, and invoking the second object recognition model to extract features of the tracked object in the test image to obtain a second test response; tracking the first test response to obtain a tracking response of the tracked object, the tracking response being used for indicating a tracking position of the tracked object in the test image; and updating the first object recognition model based on difference information between the first reference response and the second reference response, difference information between the first test response and the second test response, and difference information between the tracking label and the tracking response, which comprises: acquiring a loss optimization function corresponding to the first object recognition model; determining a value of the loss optimization function based on the difference information between the first reference response and the second reference response, the difference information between the first test response and the second test response, and the difference information between the tracking label and the tracking response; and updating the first object recognition model in such a way that the value of the loss optimization function is minimized. acquiring a template image and a test image for the training, the template image and the test image each comprising a tracked object, the test image comprising a tracking label of the tracked object, the tracking label being used for indicating a marking position of the tracked object in the test image; invoking a first object recognition model to extract features of the tracked object in the template image to obtain a first reference response, and invoking a second object recognition model to extract the features of the tracked object in the template image to obtain a second reference response, wherein each of the first object recognition model and the second object recognition model is a convolutional neural network model; invoking the first object recognition model to extract features of the tracked object in the test image to obtain a first test response, and invoking the second object recognition model to extract features of the tracked object in the test image to obtain a second test response; tracking the first test response to obtain a tracking response of the tracked object, the tracking response being used for indicating a tracking position of the tracked object in the test image; and updating the first object recognition model based on difference information between the first reference response and the second reference response, difference information between the first test response and the second test response, and difference information between the tracking label and the tracking response, which comprises: acquiring a loss optimization function corresponding to the first object recognition model; determining a value of the loss optimization function based on the difference information between the first reference response and the second reference response, the difference information between the first test response and the second test response, and the difference information between the tracking label and the tracking response; and updating the first object recognition model in such a way that the value of the loss optimization function is minimized. acquiring a loss optimization function corresponding to the first object recognition model; determining a value of the loss optimization function based on the difference information between the first reference response and the second reference response, the difference information between the first test response and the second test response, and the difference information between the tracking label and the tracking response; and updating the first object recognition model in such a way that the value of the loss optimization function is minimized.