Co-movement Pattern Mining from Videos
Summary: First study of co-movement mining from surveillance video: defines camera-based spatio-temporal proximity and proves hardness. Presents TCS-tree index, sequence-ahead pruning, sliding-window enumeration and hashing-based dominance elimination; evaluated on a 1169-camera DB, much faster than Apriori/CMC and produces GPS-comparable patterns. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dongxiang Zhang
- 2. Teng Ma
- 3. Junnan Hu
- 4. Yijun Bei
- 5. Kian-Lee Tan
- 6. Gang Chen
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,103 | Query-Aware Path Inference from Spatial Videos | 2026 | SIGMOD | 4.1945683e-05 |
| 10,382 | MAST: Towards Efficient Analytical Query Processing on Point Cloud Data | 2025 | SIGMOD | 4.1945683e-05 |
| 10,576 | Mining Platoon Patterns from Traffic Videos | 2025 | VLDB | 4.1945683e-05 |
| 10,944 | Predictive and Near-Optimal Sampling for View Materialization in Video Databases | 2024 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,718 | Swarm: Mining Relaxed Temporal Moving Object Clusters | 2010 | VLDB | 0.00010789285 |
| 1,745 | Discovery of Convoys in Trajectory Databases | 2008 | VLDB | 0.00010702338 |
| 2,520 | A Unified Approach to Route Planning for Shared Mobility | 2018 | VLDB | 8.6069685e-05 |
| 3,604 | Spatial and Temporal Constrained Ranked Retrieval over Videos | 2022 | VLDB | 6.9301368e-05 |
| 4,075 | Visual Road: A Video Data Management Benchmark | 2019 | SIGMOD | 6.4691764e-05 |
| 4,622 | A General and Parallel Platform for Mining Co-Movement Patterns over Large-scale Trajectories | 2017 | VLDB | 6.0416152e-05 |
| 4,950 | Evaluating Temporal Queries Over Video Feeds | 2021 | SIGMOD | 5.8104133e-05 |
| 7,037 | Real-time Distributed Co-Movement Pattern Detection on Streaming Trajectories | 2019 | VLDB | 4.8548122e-05 |
| 7,432 | k/2-hop: Fast Mining of Convoy Patterns With Effective Pruning | 2019 | VLDB | 4.7316373e-05 |
| 8,865 | CoMing: A Real-time Co-Movement Mining System for Streaming Trajectories | 2020 | SIGMOD | 4.4324982e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,419 | MoveMine: Mining Moving Object Databases | 2010 | SIGMOD | 4.5180464e-05 |
| 8,302 | Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories | 2014 | VLDB | 4.5435639e-05 |
| 11,992 | MoveMine 2.0: Mining Object Relationships from Movement Data | 2014 | VLDB | 4.1945683e-05 |
| 10,103 | Query-Aware Path Inference from Spatial Videos | 2026 | SIGMOD | 4.1945683e-05 |
| 8,865 | CoMing: A Real-time Co-Movement Mining System for Streaming Trajectories | 2020 | SIGMOD | 4.4324982e-05 |
| 7,037 | Real-time Distributed Co-Movement Pattern Detection on Streaming Trajectories | 2019 | VLDB | 4.8548122e-05 |
| 7,432 | k/2-hop: Fast Mining of Convoy Patterns With Effective Pruning | 2019 | VLDB | 4.7316373e-05 |
| 11,815 | Efficient Mining of Regional Movement Patterns in Semantic Trajectories | 2017 | VLDB | 4.1945683e-05 |
| 4,622 | A General and Parallel Platform for Mining Co-Movement Patterns over Large-scale Trajectories | 2017 | VLDB | 6.0416152e-05 |
| 10,576 | Mining Platoon Patterns from Traffic Videos | 2025 | VLDB | 4.1945683e-05 |