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We propose a new task of unsupervised action detection by action matching.Given two long videos, the objective is to temporally detect all pairs ofmatching video segments. A pair of video segments are matched if they share thesame human action. The task is category independent---it does not matter whataction is being performed---and no supervision is used to discover such videosegments. Unsupervised action detection by action matching allows us to alignvideos in a meaningful manner. As such, it can be used to discover new actioncategories or as an action proposal technique within, say, an action detectionpipeline. Moreover, it is a useful pre-processing step for generating videohighlights, e.g., from sports videos.  We present an effective and efficient method for unsupervised actiondetection. We use an unsupervised temporal encoding method and exploit thetemporal consistency in human actions to obtain candidate action segments. Weevaluate our method on this challenging task using three activity recognitionbenchmarks, namely, the MPII Cooking activities dataset, the THUMOS15 actiondetection benchmark and a new dataset called the IKEA dataset. On the MPIICooking dataset we detect action segments with a precision of 21.6% and recallof 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 over5094 ground truth action segment pairs.

Unsupervised Human Action Detection by Action Matching