OTIF: Efficient Tracker Pre-processing over Large Video Datasets
Summary: OTIF pre-processes large video datasets to extract all object tracks in one pass; a joint parameter tuning framework optimizes pre-processing for broad reusability. Outputs are general-purpose tracks enabling multi-query workloads with sub-second latencies, delivering 6x–25x reductions over five queries versus prior video query optimizers. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Favyen Bastani (Massachusetts Institute of Technology)
- 2. Samuel Madden (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{bastani_sigmod22,
title = {{OTIF: Efficient Tracker Pre-processing over Large Video Datasets}},
author = {Bastani, Favyen and Madden, Samuel},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517835},
url = {https://dl.acm.org/doi/10.1145/3514221.3517835},
year = {2022}
}
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Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 271 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB | 0.00022560564 |
| 281 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00022295232 |
| 1,024 | MIRIS: Fast Object Track Queries in Video | 2020 | SIGMOD | 0.00012430491 |
| 1,617 | Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine | 2019 | CIDR | 0.00010057464 |
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