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Counting repetitive actions are widely seen in human activities such asphysical exercise. Existing methods focus on performing repetitive actioncounting in short videos, which is tough for dealing with longer videos in morerealistic scenarios. In the data-driven era, the degradation of suchgeneralization capability is mainly attributed to the lack of long videodatasets. To complement this margin, we introduce a new large-scale repetitiveaction counting dataset covering a wide variety of video lengths, along withmore realistic situations where action interruption or action inconsistenciesoccur in the video. Besides, we also provide a fine-grained annotation of theaction cycles instead of just counting annotation along with a numerical value.Such a dataset contains 1,451 videos with about 20,000 annotations, which ismore challenging. For repetitive action counting towards more realisticscenarios, we further propose encoding multi-scale temporal correlation withtransformers that can take into account both performance and efficiency.Furthermore, with the help of fine-grained annotation of action cycles, wepropose a density map regression-based method to predict the action period,which yields better performance with sufficient interpretability. Our proposedmethod outperforms state-of-the-art methods on all datasets and also achievesbetter performance on the unseen dataset without fine-tuning. The dataset andcode are available.

TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting