|Cheng Zhang||The Ohio State University, USA|
|Fan Yang||the Ohio State University, USA|
|Gang Li||the Ohio State University, USA|
|Qiang Zhai||Shanghai DeepCode Robotics, P.R. China|
|Yi Jiang||Shanghai DeepCode Robotics Co., Ltd., P.R. China|
|Dong Xuan||The Ohio State University, USA|
Recently, intelligent sports analytics is becoming a hot area in both industry and academia for coaching, practicing tactic and technical analysis. With the growing trend of bringing sports analytics to live broadcasting, sports robots and common playfield, a low cost system that is easy to deploy and performs real-time and accurate sports analytics is very desirable. However , existing systems, such as Hawk-Eye, cannot satisfy these requirements due to various factors. In this paper, we present MV-Sports, a cost-effective system for real-time sports analysis based on motion and vision sensor integration. Taking tennis as a case study, we aim to recognize player shot types and measure ball states. For fine-grained player action recognition, we leverage motion signal for fast action highlighting and propose a long short term memory (LSTM)-based framework to integrate MV data for training and classification. For ball state measurement, we compute the initial ball state via motion sensing and devise an extended kalman filter (EKF)-based approach to combine ball motion physics-based tracking and vision positioning-based tracking to get more accurate ball state. We implement MV-Sports on commercial off-the-shelf (COTS) devices and conduct real-world experiments to evaluate the performance of our system. The results show our approach can achieve accurate player action recognition and ball state measurement with sub-second latency.