Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories
This work solves a domain-specific problem for sports analysts and researchers dealing with multi-agent tracking data, offering an incremental improvement over existing methods.
The paper tackles the problem of retrieving similar multi-agent spatiotemporal tracking data, such as sports plays, by addressing challenges like fine-grained location importance and permutational alignment issues, resulting in a method that boosts retrieval performance in interactive scenarios compared to state-of-the-art methods.
We propose a novel method for effective retrieval of multi-agent spatiotemporal tracking data. Retrieval of spatiotemporal tracking data offers several unique challenges compared to conventional text-based retrieval settings. Most notably, the data is fine-grained meaning that the specific location of agents is important in describing behavior. Additionally, the data often contains tracks of multiple agents (e.g., multiple players in a sports game), which generally leads to a permutational alignment problem when performing relevance estimation. Due to the frequent position swap of agents, it is difficult to maintain the correspondence of agents, and such issues make the pairwise comparison problematic for multi-agent spatiotemporal data. To address this issue, we propose a tree-based method to estimate the relevance between multi-agent spatiotemporal tracks. It uses a hierarchical structure to perform multi-agent data alignment and partitioning in a coarse-to-fine fashion. We validate our approach via user studies with domain experts. Our results show that our method boosts performance in retrieving similar sports plays -- especially in interactive situations where the user selects a subset of trajectories compared to current state-of-the-art methods.