Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors

• URL: http://arxiv.org/abs/2205.10450v1
• Date: Fri, 20 May 2022 22:14:02 GMT
• Title: Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors
• Authors: Jo\~ao V. B. Soares, Avijit Shah, Topojoy Biswas
• Abstract summary: We present a model for temporally precise action spotting in videos, which uses a dense set of detection anchors, predicting a detection confidence and corresponding fine-grained temporal displacement for each anchor. We achieve a new state-of-the-art on SoccerNet-v2, the largest soccer video dataset of its kind, with marked improvements in temporal localization.
• Score: 1.6114012813668934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a model for temporally precise action spotting in videos, which uses a dense set of detection anchors, predicting a detection confidence and corresponding fine-grained temporal displacement for each anchor. We experiment with two trunk architectures, both of which are able to incorporate large temporal contexts while preserving the smaller-scale features required for precise localization: a one-dimensional version of a u-net, and a Transformer encoder (TE). We also suggest best practices for training models of this kind, by applying Sharpness-Aware Minimization (SAM) and mixup data augmentation. We achieve a new state-of-the-art on SoccerNet-v2, the largest soccer video dataset of its kind, with marked improvements in temporal localization. Additionally, our ablations show: the importance of predicting the temporal displacements; the trade-offs between the u-net and TE trunks; and the benefits of training with SAM and mixup.

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