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Query-Aware Path Inference from Spatial Videos

Summary: Query-aware framework that avoids exhaustive trajectory reconstruction by retrieving only query-relevant snapshots via a similarity index and modeling transitions with a probability motion graph. Adds high-order spatio-temporal dependency constraints for global consistency; outperforms baselines on real and synthetic urban-video benchmarks in accuracy and efficiency. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7603
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,393 | 28.70%
DOI
10.1145/3769817

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BibTeX Citation

@inproceedings{dong_sigmod26,
        title = {{Query-Aware Path Inference from Spatial Videos}},
        author = {Dong, Taihang and Yang, Dingyu and Chen, Ping and Zhang, Dongxiang},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3769817},
        url = {https://dl.acm.org/doi/10.1145/3769817},
        year = {2026}
}

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