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

Lijun Yu, Qianyu Feng, Yijun Qian, Wenhe Liu, Alexander G. Hauptmann

Published in CVPRW, 2020


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.

  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.},