Comparative Efficiency Analysis of Data Fusion Methods for Vehicle Trajectory Reconstruction

This article compares the efficiency of vehicle trajectory analysis methods based on data fusion from multiple cameras, monitoring the same area from different views under the condition having detection errors, which causes incorrectly localized and, in some cases, undetected vehicle during the movement. The experiment used the simulation of detection and localization of vehicle moving in straight, curved, zigzag and arbitrary trajectories, with localization errors and multi-level loss of data. By comparing Kalman-filter-based method and Linear-interpolation-based method for analyzing and reconstructing vehicle trajectory, the result shows that the data loss robustness of Kalman-filter-based method is higher than that of Linear-interpolation-based method, with data loss around 97% 97% and 90% for straight, curved and zigzag trajectories respectively. However, for arbitrary trajectory, the Linear-interpolation-based method is better than Kalman-filter-based method in all levels of data loss. In conclusion, Kalman-filter-based method is effective in the case of unchanged or slight transition of direction, while Linear-interpolation-based method is effective in the case of sudden transition of direction.