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ReActNet: Temporal Localization of Repetitive Activities in Real-World Videos
[article]
2019
arXiv
pre-print
We address the problem of temporal localization of repetitive activities in a video, i.e., the problem of identifying all segments of a video that contain some sort of repetitive or periodic motion. To do so, the proposed method represents a video by the matrix of pairwise frame distances. These distances are computed on frame representations obtained with a convolutional neural network. On top of this representation, we design, implement and evaluate ReActNet, a lightweight convolutional
arXiv:1910.06096v1
fatcat:vkp43gxzpzhyxjiuzfhzbbfyoe