Road boundary estimation to improve vehicle detection and tracking in UAV video

Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle (UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection (DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory (DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%, respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.