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

By Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman. 2020

140 citations recorded by the source corpus.

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 .

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

A Short Note on the Kinetics-700 Human Action Dataset (2019, methodology): “The link also includes additional annotations for the AVA-Kinetics [7] and Countix [4] datasets.”
A Short Note on the Kinetics-700-2020 Human Action Dataset (2020, methodology): “The link also includes additional annotations for the AVA-Kinetics [7] and Countix [4] datasets.”