In this paper, we tackle the problem of video alignment, the process of
matching the frames of a pair of videos containing similar actions. The main
challenge in video alignment is that accurate correspondence should be
established despite the differences in the execution processes and appearances
between the two videos. We introduce an unsupervised method for alignment that
uses global and local features of the frames. In particular, we introduce
effective features for each video frame by means of three machine vision tools:
person detection, pose estimation, and VGG network. Then the features are
processed and combined to construct a multidimensional time series that
represent the video. The resulting time series are used to align videos of the
same actions using a novel version of dynamic time warping named Diagonalized
Dynamic Time Warping(DDTW). The main advantage of our approach is that no
training is required, which makes it applicable for any new type of action
without any need to collect training samples for it. For evaluation, we
considered video synchronization and phase classification tasks on the Penn
action dataset. Also, for an effective evaluation of the video synchronization
task, we present a new metric called Enclosed Area Error(EAE). The results show
that our method outperforms previous state-of-the-art methods, such as TCC and
other self-supervised and supervised methods.

本文介绍一种无监督的视频对准方法，通过人体检测、姿态估计、VGG 网络等机器视觉工具提取并结合构建多维时间序列，利用 Diagonalized Dynamic Time Warping (DDTW) 算法对相同动作的视频进行对齐，采用新型评价指标 Enclosed Area Error (EAE) 显示本文方法在视频对齐任务和相位分类任务上均优于之前最先进的方法 (TCC 等)。