A method for classifying a capture, wherein the capture is an image, taken by a sensor, the method comprising: - receiving a sequence (S) of a plurality of captures taken by a sensor, each of the captures comprising a plurality of elements; - generating, per capture (I 1 , I 2 ) of the sequence (S), a plurality of outputs of a convolutional neural network for classification of the capture (p r ), each of the outputs (p r ) being linked to a respective one of a plurality of predetermined classes and indicating the pseudo probability that the capture or an element of the capture is associated with the respective class; - determining, for a respective one of the captures (I 2 ) of the sequence (S), a plurality of consolidated probability values (p cs ) in dependence of a plurality of base probability values (p b ) and a plurality of context probability values (p cx ), wherein the base probability values (p b ) represent the outputs of the convolutional neural network (p r ) corresponding to the respective capture (I 2 ) and the context probability values (p cx ) represent the outputs of the convolutional neural network (p r ) corresponding to at least one further capture (I 1 ) of the sequence other than the respective capture, the context probability values (p cx ) being normalised according to a normalisation rule; - classifying the respective capture (I 2 ) or an element of the capture on the basis of the consolidated probability values (p cs ); wherein the normalisation rule comprises a softmax normalisation; wherein the consolidated probability values (p cs ) are determined by merging the base probability values (p b ) and the context probability values (p cx ) according to a merging rule. - receiving a sequence (S) of a plurality of captures taken by a sensor, each of the captures comprising a plurality of elements; - generating, per capture (I 1 , I 2 ) of the sequence (S), a plurality of outputs of a convolutional neural network for classification of the capture (p r ), each of the outputs (p r ) being linked to a respective one of a plurality of predetermined classes and indicating the pseudo probability that the capture or an element of the capture is associated with the respective class; - determining, for a respective one of the captures (I 2 ) of the sequence (S), a plurality of consolidated probability values (p cs ) in dependence of a plurality of base probability values (p b ) and a plurality of context probability values (p cx ), wherein the base probability values (p b ) represent the outputs of the convolutional neural network (p r ) corresponding to the respective capture (I 2 ) and the context probability values (p cx ) represent the outputs of the convolutional neural network (p r ) corresponding to at least one further capture (I 1 ) of the sequence other than the respective capture, the context probability values (p cx ) being normalised according to a normalisation rule; the base probability values (p b ) represent the outputs of the convolutional neural network (p r ) corresponding to the respective capture (I 2 ) and the context probability values (p cx ) represent the outputs of the convolutional neural network (p r ) corresponding to at least one further capture (I 1 ) of the sequence other than the respective capture, the context probability values (p cx ) being normalised according to a normalisation rule; - classifying the respective capture (I 2 ) or an element of the capture on the basis of the consolidated probability values (p cs ); wherein the normalisation rule comprises a softmax normalisation; wherein the consolidated probability values (p cs ) are determined by merging the base probability values (p b ) and the context probability values (p cx ) according to a merging rule. <EOS>