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We present a model for temporally precise action spotting in videos, whichuses a dense set of detection anchors, predicting a detection confidence andcorresponding fine-grained temporal displacement for each anchor. We experimentwith two trunk architectures, both of which are able to incorporate largetemporal contexts while preserving the smaller-scale features required forprecise localization: a one-dimensional version of a u-net, and a Transformerencoder (TE). We also suggest best practices for training models of this kind,by applying Sharpness-Aware Minimization (SAM) and mixup data augmentation. Weachieve a new state-of-the-art on SoccerNet-v2, the largest soccer videodataset of its kind, with marked improvements in temporal localization.Additionally, our ablations show: the importance of predicting the temporaldisplacements; the trade-offs between the u-net and TE trunks; and the benefitsof training with SAM and mixup.

Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors