1. A computer-implemented method for classifying an object occurring in a sequence of images, comprising: tracking, by a processor, the object through the sequence of images; generating, by the processor and from the sequence of images, a set of temporally distributed image crops including the object; feeding, by the processor, the set of image crops to an artificial neural network trained for classifying an object; determining, by the artificial neural network, a classification vector for each image crop; determining, by the processor, a quality measure, Q i , of each classification vector, wherein the quality measure, Q i , is determined using the formula: Q i = α · R ⁡ ( I i ) + β · 1 H ⁡ ( X ^ ⁡ ( I i ) ) in which α and β are scalars, R(I i ) is the resolution of the object's i:th image crop I i , and 
 H({circumflex over (X)}(I i )) is a Shannon entropy of the classification vector for the object's i:th image crop I i ; weighting, by the processor, the classification result for each image crop by its quality measure; and determining, by the processor, an object class for the object by combining the weighted output from the artificial neural network for the set of images, wherein the object class is an object class for which the artificial neural network is trained.