Integrated Real-Time Data Stream Analysis and Sketch-Based Video Retrieval in Team Sports

Lukas Probst, Fabian Rauschenbach, Heiko Schuldt, Philipp Seidenschwarz, Martin Rumo
In Proceedings
Appears in
Proceedings of the 2018 IEEE International Conference on Big Data (BigData'18)
Seattle, WA, USA

Big data in sports comes with two closely related challenges: first, the online analysis of continuous data streams to identify characteristic events and second, advanced retrieval in video collections and/or event data that help game analysts to search for characteristic video scenes. For both challenges, dedicated big data stream processing and retrieval systems have been developed. However, there is no infrastructure yet that integrates retrieval and automatic online data stream analysis. In this paper, we close this gap by seamlessly combining StreamTeam, our real-time team sports analysis system, and SportSense, our team sports video retrieval system, to an integrated team sports analysis infrastructure that (i) automatically detects (collaborative) events and generates statistics in real-time based on a continuous stream of raw positions, (ii) visualizes the analysis results in real-time, (iii) stores the analysis results persistently for offline activities, and (iv) leverages the stored analysis results for intuitive sketchbased video retrieval.