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We address the problem of activity detection in continuous, untrimmed videostreams. This is a difficult task that requires extracting meaningfulspatio-temporal features to capture activities, accurately localizing the startand end times of each activity. We introduce a new model, Region Convolutional3D Network (R-C3D), which encodes the video streams using a three-dimensionalfully convolutional network, then generates candidate temporal regionscontaining activities, and finally classifies selected regions into specificactivities. Computation is saved due to the sharing of convolutional featuresbetween the proposal and the classification pipelines. The entire model istrained end-to-end with jointly optimized localization and classificationlosses. R-C3D is faster than existing methods (569 frames per second on asingle Titan X Maxwell GPU) and achieves state-of-the-art results on THUMOS'14.We further demonstrate that our model is a general activity detection frameworkthat does not rely on assumptions about particular dataset properties byevaluating our approach on ActivityNet and Charades. Our code is available athttp://ai.bu.edu/r-c3d/.

R-C3D: Region Convolutional 3D Network for Temporal Activity Detection