  Re-identifying a person across multiple disjoint camera views is important
for intelligent video surveillance, smart retailing and many other
applications. However, existing person re-identification (ReID) methods are
challenged by the ubiquitous occlusion over persons and suffer from performance
degradation. This paper proposes a novel occlusion-robust and alignment-free
model for occluded person ReID and extends its application to realistic and
crowded scenarios. The proposed model first leverages the full convolution
network (FCN) and pyramid pooling to extract spatial pyramid features. Then an
alignment-free matching approach, namely Foreground-aware Pyramid
Reconstruction (FPR), is developed to accurately compute matching scores
between occluded persons, despite their different scales and sizes. FPR uses
the error from robust reconstruction over spatial pyramid features to measure
similarities between two persons. More importantly, we design an
occlusion-sensitive foreground probability generator that focuses more on clean
human body parts to refine the similarity computation with less contamination
from occlusion. The FPR is easily embedded into any end-to-end person ReID
models. The effectiveness of the proposed method is clearly demonstrated by the
experimental results (Rank-1 accuracy) on three occluded person datasets:
Partial REID (78.30\%), Partial iLIDS (68.08\%) and Occluded REID (81.00\%);
and three benchmark person datasets: Market1501 (95.42\%), DukeMTMC (88.64\%)
and CUHK03 (76.08\%)
