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arXiv · 2006.15418

Counting Out Time: Class Agnostic Video Repetition Counting in the Wild

Debidatta Dwibedi·Yusuf Aytar·Jonathan Tompson·Pierre Sermanet·Andrew Zisserman
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Abstract

We present an approach for estimating the period with which an action is repeated in a video. The crux of the approach lies in constraining the period prediction module to use temporal self-similarity as an intermediate representation bottleneck that allows generalization to unseen repetitions in videos in the wild. We train this model, called Repnet, with a synthetic dataset that is generated from a large unlabeled video collection by sampling short clips of varying lengths and repeating them with different periods and counts. This combination of synthetic data and a powerful yet constrained model, allows us to predict periods in a class-agnostic fashion. Our model substantially exceeds the state of the art performance on existing periodicity (PERTUBE) and repetition counting (QUVA) benchmarks. We also collect a new challenging dataset called Countix (~90 times larger than existing datasets) which captures the challenges of repetition counting in real-world videos. Project webpage: https://sites.google.com/view/repnet .

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Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman. 2020-06-27. Counting Out Time: Class Agnostic Video Repetition Counting in the Wild. https://arxiv.org/abs/2006.15418

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