We address the person re-identification problem by effectively exploiting a
globally discriminative feature representation from a sequence of tracked human
regions/patches. This is in contrast to previous person re-id works, which rely
on either single frame based person to person patch matching, or graph based
sequence to sequence matching. We show that a progressive/sequential fusion
framework based on long short term memory (LSTM) network aggregates the
frame-wise human region representation at each time stamp and yields a sequence
level human feature representation. Since LSTM nodes can remember and propagate
previously accumulated good features and forget newly input inferior ones, even
with simple hand-crafted features, the proposed recurrent feature aggregation
network (RFA-Net) is effective in generating highly discriminative sequence
level human representations. Extensive experimental results on two person
re-identification benchmarks demonstrate that the proposed method performs
favorably against state-of-the-art person re-identification methods.

本文提出一种基于 LSTM 网络的逐步 / 顺序融合框架的人物重新识别方法，利用一系列追踪人体区域 / 补丁的全局判别特征表示产生序列级别的高度区分人类特征表示，并在两个人物重新识别基准测试上取得了良好的效果