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)
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Authors
- 1. Taihang Dong (Zhejiang University)
- 2. Dingyu Yang (Zhejiang University)
- 3. Ping Chen (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang Lab; Zhejiang University)
- 4. Dongxiang Zhang (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
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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