SVQ++: Querying for Object Interactions in Video Streams
Summary: SVQ++ enables declarative querying over real-time video streams for object interactions. It employs Progressive Filters to cheaply detect target objects and an Interaction Sheave to prune non-interacting frames, delivering up to 100× throughput gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daren Chao
- 2. Nick Koudas
- 3. Ioannis Xarchakos
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,604 | Spatial and Temporal Constrained Ranked Retrieval over Videos | 2022 | VLDB | 6.9301368e-05 |
| 4,950 | Evaluating Temporal Queries Over Video Feeds | 2021 | SIGMOD | 5.8104133e-05 |
| 6,130 | VOCAL: Video Organization and Interactive Compositional AnaLytics | 2022 | CIDR | 5.1962107e-05 |
| 8,247 | Query-Driven Video Event Processing for the Internet of Multimedia Things | 2021 | VLDB | 4.5511197e-05 |
| 8,383 | EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions | 2023 | VLDB | 4.5307128e-05 |
| 9,768 | DoveDB: A Declarative and Low-Latency Video Database | 2023 | VLDB | 4.2856106e-05 |
| 11,061 | Optimizing Video Queries with Declarative Clues | 2024 | VLDB | 4.1945683e-05 |
| 11,284 | EQUI-VOCAL Demonstration: Synthesizing Video Queries from User Interactions | 2023 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 316 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB | 0.00027988668 |
| 3,620 | SVQ: Streaming Video Queries | 2019 | SIGMOD | 6.9084299e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,672 | Optimizing Video Selection LIMIT Queries With Commonsense Knowledge | 2024 | VLDB | 4.4710897e-05 |
| 9,341 | SketchQL: Video Moment Querying with a Visual Query Interface | 2024 | SIGMOD | 4.3554097e-05 |
| 3,604 | Spatial and Temporal Constrained Ranked Retrieval over Videos | 2022 | VLDB | 6.9301368e-05 |
| 8,383 | EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions | 2023 | VLDB | 4.5307128e-05 |
| 316 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB | 0.00027988668 |
| 8,287 | DeepVQL: Deep Video Queries on PostgreSQL | 2023 | VLDB | 4.5435639e-05 |
| 11,061 | Optimizing Video Queries with Declarative Clues | 2024 | VLDB | 4.1945683e-05 |
| 4,950 | Evaluating Temporal Queries Over Video Feeds | 2021 | SIGMOD | 5.8104133e-05 |
| 8,277 | TQVS: Temporal Queries over Video Streams in Action | 2020 | SIGMOD | 4.5436203e-05 |
| 3,620 | SVQ: Streaming Video Queries | 2019 | SIGMOD | 6.9084299e-05 |