Dynamic Occlusion–Predictive Neural Network for Robust Roadside Multi-Vehicle Tracking
Shuai Wang, Yafei Wang, Bowen Wang, Chongfeng Wei, Hao Liu · 2026
Read Dynamic Occlusion–Predictive Neural Network for Robust Roadside Multi-Vehicle Tracking by Shuai Wang, Yafei Wang, Bowen Wang, Chongfeng Wei, Hao Liu (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13259315.txt.
Description
Shuai Wang and colleagues track roadside traffic through heavy occlusion in a 2026 paper. Vehicles hide each other from fixed cameras, breaking tracks and swapping identities. Their system predicts how occluded each target will become, rather than reacting after the fact.
A transformer forecasts occlusion trends from each vehicle history. A graph module then reasons from road layout and neighbor positions to infer hidden vehicles. Virtual states keep invisible targets linked into continuous tracks. Prediction feeds directly into the training objective as a weighting signal. Traffic operators gain steadier vehicle counts and paths at crowded junctions with blind spots.
Catalog record
| CREATOR | Shuai Wang, Yafei Wang, Bowen Wang, Chongfeng Wei, Hao Liu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 63 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |