We propose a new task of unsupervised action detection by action matching.
Given two long videos, the objective is to temporally detect all pairs of
matching video segments. A pair of video segments are matched if they share the
same human action. The task is category independent---it does not matter what
action is being performed---and no supervision is used to discover such video
segments. Unsupervised action detection by action matching allows us to align
videos in a meaningful manner. As such, it can be used to discover new action
categories or as an action proposal technique within, say, an action detection
pipeline. Moreover, it is a useful pre-processing step for generating video
highlights, e.g., from sports videos.
We present an effective and efficient method for unsupervised action
detection. We use an unsupervised temporal encoding method and exploit the
temporal consistency in human actions to obtain candidate action segments. We
evaluate our method on this challenging task using three activity recognition
benchmarks, namely, the MPII Cooking activities dataset, the THUMOS15 action
detection benchmark and a new dataset called the IKEA dataset. On the MPII
Cooking dataset we detect action segments with a precision of 21.6% and recall
of 11.7% over 946 long video pairs and over 5000 ground truth action segments.
Similarly, on THUMOS dataset we obtain 18.4% precision and 25.1% recall over
5094 ground truth action segment pairs.

本文提出了一种新的无监督行为检测方法，通过行为匹配检测共享相同人类行为的视频片段，该方法可用于发现新的行为类别、作为活动检测流程中的行为提议技术，同时还是生成视频重点的有用预处理步骤，作者在 MPII Cooking 数据集、THUMOS15 行为检测基准和 IKEA 数据集上使用三个活动识别基准评估了该方法。