Zero-VIRUS: Zero-shot VehIcle Route Understanding System for Intelligent Transportation

IEEE CVF

Nowadays, understanding the traffic statistics in real city-scale camera networks takes an important place in the intelligent transportation field. Recently, vehicle route understanding brings a new challenge to the area. It aims to measure the traffic density by identifying the route of each vehicle in traffic cameras. This year, the AI City Challenge holds a competition with real-world traffic data on vehicle route understanding, which requires both efficiency and effectiveness. In this work, we propose Zero-VIRUS, a Zero-shot VehIcle Route Understanding System, which requires no annotation for vehicle tracklets and is applicable for the changeable real-world traffic scenarios. It adopts a novel 2D field modeling of pre-defined routes to estimate the proximity and completeness of each track. The proposed system has achieved third place on Dataset A in stage 1 of the competition (Track 1: Vehicle Counts by Class at Multiple Intersections) against world-wide participants on both effectiveness and efficiency, with a record of the top place on 50% of the test set.

@inproceedings{yu2020zero,
  title={ {Zero-VIRUS}: Zero-shot VehIcle Route Understanding System for Intelligent Transportation},
  author={Yu, Lijun and Feng, Qianyu and Qian, Yijun and Liu, Wenhe and Hauptmann, Alexander G.},
  booktitle={CVPRW},
  year={2020}
}