Matching pedestrians across multiple camera views, known as human
re-identification, is a challenging research problem that has numerous
applications in visual surveillance. With the resurgence of Convolutional
Neural Networks (CNNs), several end-to-end deep Siamese CNN architectures have
been proposed for human re-identification with the objective of projecting the
images of similar pairs (i.e. same identity) to be closer to each other and
those of dissimilar pairs to be distant from each other. However, current
networks extract fixed representations for each image regardless of other
images which are paired with it and the comparison with other images is done
only at the final level. In this setting, the network is at risk of failing to
extract finer local patterns that may be essential to distinguish positive
pairs from hard negative pairs. In this paper, we propose a gating function to
selectively emphasize such fine common local patterns by comparing the
mid-level features across pairs of images. This produces flexible
representations for the same image according to the images they are paired
with. We conduct experiments on the CUHK03, Market-1501 and VIPeR datasets and
demonstrate improved performance compared to a baseline Siamese CNN
architecture.

本文提出了一种用于跨摄像头匹配行人的人体再识别框架，通过引入一个选择性强化细粒度共有的局部模式的门控函数，将同一身份的图像向量嵌入到特征空间中更密集的区域，提高了再识别的性能。在 CUHK03、Market-1501 和 VIPeR 数据集上的实验证明，与基线 Siamese CNN 架构相比，该框架表现更优。

基于门控连体卷积神经网络的人体再识别架构

Gated Siamese Convolutional Neural Network Architecture for Human  Re-Identification

Matching pedestrians across multiple camera views known as human
re-identification (re-identification) is a challenging problem in visual
surveillance. In the existing works concentrating on feature extraction,
representations are formed locally and independent of other regions. We present
a novel siamese Long Short-Term Memory (LSTM) architecture that can process
image regions sequentially and enhance the discriminative capability of local
feature representation by leveraging contextual information. The feedback
connections and internal gating mechanism of the LSTM cells enable our model to
memorize the spatial dependencies and selectively propagate relevant contextual
information through the network. We demonstrate improved performance compared
to the baseline algorithm with no LSTM units and promising results compared to
state-of-the-art methods on Market-1501, CUHK03 and VIPeR datasets.
Visualization of the internal mechanism of LSTM cells shows meaningful patterns
can be learned by our method.

本文提出了一种新颖的孪生 LSTM 结构，可以顺序地处理图像区域，并通过利用上下文信息增强局部特征表示的区分能力，有效地解决了多个摄像头视图下的行人匹配问题，取得了与最先进方法相媲美的良好效果，为视觉监控领域的研究提供了新思路。