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Spatio-temporal action detection is an important and challenging problem invideo understanding. The existing action detection benchmarks are limited inaspects of small numbers of instances in a trimmed video or low-level atomicactions. This paper aims to present a new multi-person dataset ofspatio-temporal localized sports actions, coined as MultiSports. We firstanalyze the important ingredients of constructing a realistic and challengingdataset for spatio-temporal action detection by proposing three criteria: (1)multi-person scenes and motion dependent identification, (2) with well-definedboundaries, (3) relatively fine-grained classes of high complexity. Based onthese guide-lines, we build the dataset of MultiSports v1.0 by selecting 4sports classes, collecting 3200 video clips, and annotating 37701 actioninstances with 902k bounding boxes. Our datasets are characterized withimportant properties of high diversity, dense annotation, and high quality. OurMulti-Sports, with its realistic setting and detailed annotations, exposes theintrinsic challenges of spatio-temporal action detection. To benchmark this, weadapt several baseline methods to our dataset and give an in-depth analysis onthe action detection results in our dataset. We hope our MultiSports can serveas a standard benchmark for spatio-temporal action detection in the future. Ourdataset website is at https://deeperaction.github.io/multisports/.

MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions