Feature extraction and matching are two crucial components in person
Re-Identification (ReID). The large pose deformations and the complex view
variations exhibited by the captured person images significantly increase the
difficulty of learning and matching of the features from person images. To
overcome these difficulties, in this work we propose a Pose-driven Deep
Convolutional (PDC) model to learn improved feature extraction and matching
models from end to end. Our deep architecture explicitly leverages the human
part cues to alleviate the pose variations and learn robust feature
representations from both the global image and different local parts. To match
the features from global human body and local body parts, a pose driven feature
weighting sub-network is further designed to learn adaptive feature fusions.
Extensive experimental analyses and results on three popular datasets
demonstrate significant performance improvements of our model over all
published state-of-the-art methods.

该论文提出了一种基于姿态驱动的卷积神经网络模型，利用人体部位信息从全局及局部层面进行特征提取和匹配，进一步设计了自适应特征融合子网络，实现了对行人重识别任务中姿态变化的准确探测和学习，在三个公开数据集上均超越了现有的最佳方法。