The choice of poses for camera calibration with planar patterns is only
rarely considered - yet the calibration precision heavily depends on it. This
work presents a pose selection method that finds a compact and robust set of
calibration poses and is suitable for interactive calibration. Consequently,
singular poses that would lead to an unreliable solution are avoided
explicitly, while poses reducing the uncertainty of the calibration are
favoured. For this, we use uncertainty propagation.
Our method takes advantage of a self-identifying calibration pattern to track
the camera pose in real-time. This allows to iteratively guide the user to the
target poses, until the desired quality level is reached. Therefore, only a
sparse set of key-frames is needed for calibration.
The method is evaluated on separate training and testing sets, as well as on
synthetic data. Our approach performs better than comparable solutions while
requiring 30% less calibration frames.

该论文提出了一种姿态选择方法，利用自我识别校准模式实时跟踪相机姿态从而指导用户到达目标姿态，使用不确定性传播避免一些不准确的姿态，并有助于提高相机校准的准确性，其可行性和优越性得到了实验证明。

交互式相机标定的有效姿态选择

Efficient Pose Selection for Interactive Camera Calibration

Most of human actions consist of complex temporal compositions of more simple
actions. Action recognition tasks usually relies on complex handcrafted
structures as features to represent the human action model. Convolutional
Neural Nets (CNN) have shown to be a powerful tool that eliminate the need for
designing handcrafted features. Usually, the output of the last layer in CNN (a
layer before the classification layer -known as fc7) is used as a generic
feature for images. In this paper, we show that fc7 features, per se, can not
get a good performance for the task of action recognition, when the network is
trained only on images. We present a feature structure on top of fc7 features,
which can capture the temporal variation in a video. To represent the temporal
components, which is needed to capture motion information, we introduced a
hierarchical structure. The hierarchical model enables to capture sub-actions
from a complex action. At the higher levels of the hierarchy, it represents a
coarse capture of action sequence and lower levels represent fine action
elements. Furthermore, we introduce a method for extracting key-frames using
binary coding of each frame in a video, which helps to improve the performance
of our hierarchical model. We experimented our method on several action
datasets and show that our method achieves superior results compared to other
state-of-the-arts methods.

本文介绍一种基于卷积神经网络的层级模型，用于动作识别任务。该模型采用最后一层卷积神经网络的输出特征作为基础，并设计了一个层级结构来捕获视频中的时间变化。同时，作者引入了一种用于提取视频关键帧的方法，从而提高了模型的性能表现。通过在多个动作数据集上的实验结果表明，该方法在动作识别任务上取得了优越的性能表现。