It is well known that the current types of neural networks perform quite well in identifying faces in (still) images. But what about re-identifying moving pedestrians in non-overlapping video sequences taken from different cameras? The paper's novel approach increases the accuracy of re-identification (here: rank-1 recognition rate and mean average precision) by 40.8 percent and 4.2 percent, respectively, compared to the best alternative video-based and image-based algorithms. This is essentially achieved by the following two innovative design choices for their 56-layer (generalized) convolutional neural network with 3.2 million training parameters.
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