A method for classifying a capture, wherein the capture is an image, taken by a sensor, the method comprising: - receiving a sequence 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, 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, 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 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, 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, 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.