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This paper presents the baseline method proposed for the Sports Video taskpart of the MediaEval 2022 benchmark. This task proposes two subtasks: strokeclassification from trimmed videos, and stroke detection from untrimmed videos.This baseline addresses both subtasks. We propose two types of 3D-CNNarchitectures to solve the two subtasks. Both 3D-CNNs use Spatio-temporalconvolutions and attention mechanisms. The architectures and the trainingprocess are tailored to solve the addressed subtask. This baseline method isshared publicly online to help the participants in their investigation andalleviate eventually some aspects of the task such as video processing,training method, evaluation and submission routine. The baseline method reaches86.4% of accuracy with our v2 model for the classification subtask. For thedetection subtask, the baseline reaches a mAP of 0.131 and IoU of 0.515 withour v1 model.

Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms