ReActNet: Temporal Localization of Repetitive Activities in Real-World Videos
This addresses the challenge of identifying repetitive motions in real-world videos for applications like video analysis, with incremental improvements over existing approaches by relaxing assumptions about video contents and activity types.
The paper tackles the problem of temporally localizing repetitive activities in videos by proposing ReActNet, a lightweight CNN that classifies frames based on pairwise frame distances, achieving superior performance compared to state-of-the-art methods on recent datasets.
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 neural network that classifies a given frame as belonging (or not) to a repetitive video segment. An important property of the employed representation is that it can handle repetitive segments of arbitrary number and duration. Furthermore, the proposed training process requires a relatively small number of annotated videos. Our method raises several of the limiting assumptions of existing approaches regarding the contents of the video and the types of the observed repetitive activities. Experimental results on recent, publicly available datasets validate our design choices, verify the generalization potential of ReActNet and demonstrate its superior performance in comparison to the current state of the art.