MIRIS: Fast Object Track Queries in Video
Summary: Query-driven tracking fuses object-tracking with predicate evaluation to accelerate object-track queries in video databases. Adaptive framerate: low by default, upsampled as needed for accuracy, achieving ~9x speedups over the IOU tracker. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Favyen Bastani (Massachusetts Institute of Technology)
- 2. Songtao He (Massachusetts Institute of Technology)
- 3. Arjun Balasingam (Massachusetts Institute of Technology)
- 4. Karthik Gopalakrishnan (Massachusetts Institute of Technology)
- 5. Mohammad Alizadeh (Massachusetts Institute of Technology)
- 6. Hari Balakrishnan (Massachusetts Institute of Technology)
- 7. Michael Cafarella (Massachusetts Institute of Technology)
- 8. Tim Kraska (Massachusetts Institute of Technology)
- 9. Sam Madden (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{bastani_sigmod20,
title = {{MIRIS: Fast Object Track Queries in Video}},
author = {Bastani, Favyen and He, Songtao and Balasingam, Arjun and Gopalakrishnan, Karthik and Alizadeh, Mohammad and Balakrishnan, Hari and Cafarella, Michael and Kraska, Tim and Madden, Sam},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389692},
url = {https://dl.acm.org/doi/10.1145/3318464.3389692},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 36 of 36 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 284 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB | 0.00022370521 |
| 295 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00022238183 |
| 1,607 | Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine | 2019 | CIDR | 0.0001022751 |
| 2,384 | DeepLens: Towards a Visual Data Management System | 2019 | CIDR | 8.6494525e-05 |
| 3,410 | SVQ: Streaming Video Queries | 2019 | SIGMOD | 7.4362585e-05 |
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