Segmentation is Tracking: Spatial-Temporal Map Vehicle Trajectory Reconstruction and Validation

This paper proposed the “Segmentation is traCKing” (SiCK) solution that extract vehicle movements with semantic segmentation for high-resolution trajectory reconstruction and validation. The dynamic mode decomposition (DMD) method extracts vehicle strands by decomposing the spatial-temporal map (STMap) into the sparse foreground and low-rank background. The Res-UNet+ neural networks was designed by adapting two prevalent deep learning architectures, which significantly improved the performance of the STMap-based vehicle detection and tracking. The DMD model provides many interesting insights for understanding the evolution of underlying spatial-temporal structures preserved by the STMap. The model outputs were compared with the previous image processing model and baseline semantic segmentation neural networks. After thorough evaluations, this solution is accurate and robust against many challenging factors. Finally, this work addresses data quality issues by correcting erroneous trajectories obtained from computer vision tools. Extracting high-fidelity vehicle trajectories for transportation scientific research is a systematic process. This new framework can significantly improve the accuracy and reliability of video-based trajectory data acquisition.