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\%)

本文提出了一种抵御遮挡和无需精确定位的人物重识别方法，并扩展其应用于更为真实和拥挤的场景中，在三个挑战性遮挡数据集和三个基准人物数据集上获得了极高的准确率。

无需对齐的遮挡人物重新识别前景感知金字塔重建

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

A novel efficient method for extraction of object proposals is introduced.
Its "objectness" function exploits deep spatial pyramid features, a novel
fast-to-compute HoG-based edge statistic and the EdgeBoxes score. The
efficiency is achieved by the use of spatial bins in a novel combination with
sparsity-inducing group normalized SVM. State-of-the-art recall performance is
achieved on Pascal VOC07, significantly outperforming methods with comparable
speed. Interestingly, when only 100 proposals per image are considered the
method attains 78% recall on VOC07. The method improves mAP of the RCNN
state-of-the-art class-specific detector, increasing it by 10 points when only
50 proposals are used in each image. The system trained on twenty classes
performs well on the two hundred class ILSVRC2013 set confirming generalization
capability.

本研究提出了一种高效的目标提取方法，通过利用深度空间金字塔特征、基于 HoG 的新型快速计算边缘统计和 EdgeBoxes 得分的 'objectness' 函数，以及与稀疏感知组归一化 SVM 结合的新型空间分 bin 方式，达到了与同等速度方法相比更高的 Pascal VOC07 的回收性能，甚至仅考虑每个图像 100 个提议时，方法仍达到了 78％的召回率，并且在使用每个图像中仅 50 个提议时，改善了 RCNN 最先进的类别特定检测器的 mAP，提高了 10 个百分点。该系统对于全球 200 个类别在 ILSVRC2013 数据集中的检测表现良好，证实了其泛化能力。