As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight. However, one of the major challenges is the difficulty of automated acquisition of continuous movement data during sports matches. In particular, it is a conundrum to reliably track a tiny ball on a wide soccer pitch with obstacles such as occlusion and imitations. Tackling the problem, this paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. We combine Set Transformers to get permutation-invariant and equivariant representations of the multi-agent contexts with a hierarchical architecture that intermediately predicts the player ball possession to support the final trajectory inference. Also, we introduce the reality loss term and postprocessing to secure the estimated trajectories to be physically realistic. The experimental results show that our model provides natural and accurate trajectories as well as admissible player ball possession at the same time. Lastly, we suggest several practical applications of our framework including missing trajectory imputation, semi-automated pass annotation, automated zoom-in for match broadcasting, and calculating possession-wise running performance metrics.

Many advancements in computer vision and machine learning have shown potential for significantly improving the lives of people with disabilities. In particular, recent research has demonstrated that deep neural network models could be used to bridge the gap between the deaf who use sign language and hearing people. The major impediment to advancing such models is the lack of high-quality and large-scale training data. Moreover, previously released sign language datasets include few or no interrogative sentences compared to declarative sentences. In this paper, we introduce a new publicly available large-scale Korean Sign Language (KSL) dataset-KSL-Guide-that includes both declarative sentences and comparable interrogative sentences, which are required for a model to achieve high performance in real-world interactive tasks deployed on service applications. Our dataset contains a total of 121K sign language video samples featuring sentences and words spoken by native KSL speakers with extensive annotations (e.g., gloss, translation, keypoints, and timestamps). We exploit a multi-camera system to produce 3D human pose keypoints as well as 2D keypoints from multi-view RGB. Our experiments quantitatively demonstrate that the inclusion of interrogative sentences in training for sign language recognition and translation tasks greatly improves their performance. Furthermore, we empirically show the qualitative results by developing a prototype application using our dataset, providing an interactive guide service that helps to lower the communication barrier between sign language speakers and hearing people.