Abstract. First, we review the part detectors we created in the re-id bookchapter. Second, we review the structure of CNNs. The connection between these two topics is this idea: the first convolutional layer of a CNN is similar to a bank of spatial filters, while the part detectors are based on histograms of oriented gradients (HOG) features: is there some transferable knowledge between the two approaches? a new type of layer for the CNN? a new type of feature extraction for HOGs? what about the following convolutional layers in a CNN?