Person Re-identification Using Spatial and Layer-Wise Attention

Person re-identification requires extraction of dis-
criminative features to ensure a correct match; this must be
done independent of challenges, such as occlusion, view, or
lighting changes. While occlusion can be eliminated by changing
the camera setup from a horizontal to a vertical (overhead)
viewpoint, other challenges arise as the total visible surface
area of persons is decreased. As a result, methods that focus
on the most discriminative regions of persons must be applied,
while different domains should also be considered to extract
different semantics. To further increase feature discriminability,
complementary features extracted at different abstraction levels
should be fused. To emphasize features at certain abstraction
levels depending on the input, fusion should be done intel-
ligently. This work considers multiple domains and feature
discrimination, where a multimodal convolution neural network
is applied to fuse RGB and depth information. To extract multi-
local discriminative features, two different attention modules are
proposed: (1) a spatial attention module, which is able to capture
local information at different abstraction levels, and (2) a layer-
wise attention module, which works as a dynamic weighting
scheme to assign weights and fuse local abstraction-level features
intelligently, depending on the input image. By fusing local and
global features in a multimodal context, we show state-of-the-art
accuracies on two publicly available datasets, DPI-T and TVPR,
while increasing the state-of-the-art accuracy on a third dataset,
OPR. Finally, through both visual and quantitative analysis, we
show the ability of the proposed system to leverage multiple
frames, by adapting feature weighting depending on the input.