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Re-identifying a person across multiple disjoint camera views is importantfor intelligent video surveillance, smart retailing and many otherapplications. However, existing person re-identification (ReID) methods arechallenged by the ubiquitous occlusion over persons and suffer from performancedegradation. This paper proposes a novel occlusion-robust and alignment-freemodel for occluded person ReID and extends its application to realistic andcrowded scenarios. The proposed model first leverages the full convolutionnetwork (FCN) and pyramid pooling to extract spatial pyramid features. Then analignment-free matching approach, namely Foreground-aware PyramidReconstruction (FPR), is developed to accurately compute matching scoresbetween occluded persons, despite their different scales and sizes. FPR usesthe error from robust reconstruction over spatial pyramid features to measuresimilarities between two persons. More importantly, we design anocclusion-sensitive foreground probability generator that focuses more on cleanhuman body parts to refine the similarity computation with less contaminationfrom occlusion. The FPR is easily embedded into any end-to-end person ReIDmodels. The effectiveness of the proposed method is clearly demonstrated by theexperimental 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\%)

Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification