Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories Article Swipe
Long Sha
,
Patrick Lucey
,
Stephan Zheng
,
Tae-Hwan Kim
,
Yisong Yue
,
Sridha Sridharan
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1710.02255
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1710.02255
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1710.02255
- https://arxiv.org/pdf/1710.02255
- OA Status
- green
- Cited By
- 14
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2762002323
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2762002323Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1710.02255Digital Object Identifier
- Title
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Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of TrajectoriesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-10-06Full publication date if available
- Authors
-
Long Sha, Patrick Lucey, Stephan Zheng, Tae-Hwan Kim, Yisong Yue, Sridha SridharanList of authors in order
- Landing page
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https://arxiv.org/abs/1710.02255Publisher landing page
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https://arxiv.org/pdf/1710.02255Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1710.02255Direct OA link when available
- Concepts
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Computer science, Pairwise comparison, Relevance (law), Tree (set theory), Domain (mathematical analysis), Data mining, Artificial intelligence, Information retrieval, Machine learning, Political science, Law, Mathematical analysis, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
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2022: 1, 2021: 4, 2020: 5, 2019: 3, 2018: 1Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.fashion. | 135 |
| abstract_inverted_index.frequent | 77 |
| abstract_inverted_index.location | 38 |
| abstract_inverted_index.maintain | 86 |
| abstract_inverted_index.methods. | 176 |
| abstract_inverted_index.multiple | 53, 56 |
| abstract_inverted_index.notably, | 29 |
| abstract_inverted_index.pairwise | 96 |
| abstract_inverted_index.position | 78 |
| abstract_inverted_index.specific | 37 |
| abstract_inverted_index.tracking | 11, 16 |
| abstract_inverted_index.validate | 137 |
| abstract_inverted_index.Retrieval | 13 |
| abstract_inverted_index.alignment | 68, 129 |
| abstract_inverted_index.behavior. | 45 |
| abstract_inverted_index.difficult | 84 |
| abstract_inverted_index.effective | 6 |
| abstract_inverted_index.generally | 63 |
| abstract_inverted_index.important | 42 |
| abstract_inverted_index.relevance | 72, 115 |
| abstract_inverted_index.retrieval | 7, 26 |
| abstract_inverted_index.settings. | 27 |
| abstract_inverted_index.structure | 124 |
| abstract_inverted_index.challenges | 21 |
| abstract_inverted_index.comparison | 97 |
| abstract_inverted_index.describing | 44 |
| abstract_inverted_index.especially | 160 |
| abstract_inverted_index.performing | 71 |
| abstract_inverted_index.retrieving | 155 |
| abstract_inverted_index.situations | 163 |
| abstract_inverted_index.text-based | 25 |
| abstract_inverted_index.tree-based | 110 |
| abstract_inverted_index.estimation. | 73 |
| abstract_inverted_index.interactive | 162 |
| abstract_inverted_index.multi-agent | 9, 100, 117, 127 |
| abstract_inverted_index.performance | 153 |
| abstract_inverted_index.problematic | 98 |
| abstract_inverted_index.conventional | 24 |
| abstract_inverted_index.fine-grained | 33 |
| abstract_inverted_index.hierarchical | 123 |
| abstract_inverted_index.partitioning | 131 |
| abstract_inverted_index.trajectories | 171 |
| abstract_inverted_index.Additionally, | 46 |
| abstract_inverted_index.permutational | 67 |
| abstract_inverted_index.coarse-to-fine | 134 |
| abstract_inverted_index.correspondence | 88 |
| abstract_inverted_index.spatiotemporal | 10, 15, 101, 118 |
| abstract_inverted_index.state-of-the-art | 175 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |