Variations in visual factors such as viewpoint, pose, illumination and
background, are usually viewed as important challenges in person
re-identification (re-ID). In spite of acknowledging these factors to be
influential, quantitative studies on how they affect a re-ID system are still
lacking. To derive insights in this scientific campaign, this paper makes an
early attempt in studying a particular factor, viewpoint. We narrow the
viewpoint problem down to the pedestrian rotation angle to obtain focused
conclusions. In this regard, this paper makes two contributions to the
community. First, we introduce a large-scale synthetic data engine, PersonX.
Composed of hand-crafted 3D person models, the salient characteristic of this
engine is "controllable". That is, we are able to synthesize pedestrians by
setting the visual variables to arbitrary values. Second, on the 3D data
engine, we quantitatively analyze the influence of pedestrian rotation angle on
re-ID accuracy. Comprehensively, the person rotation angles are precisely
customized from 0 to 360, allowing us to investigate its effect on the
training, query, and gallery sets. Extensive experiment helps us have a deeper
understanding of the fundamental problems in person re-ID. Our research also
provides useful insights for dataset building and future practical usage, e.g.,
a person of a side view makes a better query.

本研究通过引入可控的整合 3D 人体模型的大规模数据引擎 PersonX，对行人旋转角度对行人再识别准确性的影响进行了定量分析，从而深入研究了旋转角度等视觉因素对行人再识别系统的影响。

从视角的角度解析人员再识别

Dissecting Person Re-identification from the Viewpoint of Viewpoint

Scene text recognition plays an important role in many computer vision
applications. The small size of available public available scene text datasets
is the main challenge when training a text recognition CNN model. In this
paper, we propose a CNN based Chinese text recognition algorithm. To enlarge
the dataset for training the CNN model, we design a synthetic data engine for
Chinese scene character generation, which generates representative character
images according to the fonts use frequency of Chinese texts. As the Chinese
text is more complex, the English text recognition CNN architecture is modified
for Chinese text. To ensure the small size nature character dataset and the
large size artificial character dataset are comparable in training, the CNN
model are trained progressively. The proposed Chinese text recognition
algorithm is evaluated with two Chinese text datasets. The algorithm achieves
better recognize accuracy compared to the baseline methods.

本文提出了一种基于卷积神经网络的中文文本识别算法，并设计了一种合成数据引擎，用于生成代表性的中文场景字符图像来扩大数据集。通过对中文文本识别 CNN 架构进行修改，本算法在两个中文文本数据集上得到了更好的识别精度为基准方法。