  In order to resist the adverse effect of viewpoint variations for improving
vehicle re-identification performance, we design quadruple directional deep
learning networks to extract quadruple directional deep learning features
(QD-DLF) of vehicle images. The quadruple directional deep learning networks
are with similar overall architecture, including the same basic deep learning
architecture but different directional feature pooling layers. Specifically,
the same basic deep learning architecture is a shortly and densely connected
convolutional neural network to extract basic feature maps of an input square
vehicle image in the first stage. Then, the quadruple directional deep learning
networks utilize different directional pooling layers, i.e., horizontal average
pooling (HAP) layer, vertical average pooling (VAP) layer, diagonal average
pooling (DAP) layer and anti-diagonal average pooling (AAP) layer, to compress
the basic feature maps into horizontal, vertical, diagonal and anti-diagonal
directional feature maps, respectively.
  Finally, these directional feature maps are spatially normalized and
concatenated together as a quadruple directional deep learning feature for
vehicle re-identification. Extensive experiments on both VeRi and VehicleID
databases show that the proposed QD-DLF approach outperforms multiple
state-of-the-art vehicle re-identification methods.
