Spatial-Temporal Map Vehicle Trajectory Detection Using Dynamic Mode Decomposition and Res-UNet+ Neural Networks
This provides a reliable solution for video-based trajectory extraction, supporting traffic flow and vehicle control research, but it is incremental as it builds on existing methods like DMD and neural networks.
The paper tackled the problem of extracting vehicle trajectories from traffic camera videos by developing a method combining Dynamic Mode Decomposition and a Res-UNet+ neural network, resulting in accurate and robust trajectory detection that addressed quality issues in existing data and published cleaned high-quality trajectory data.
This paper presents a machine-learning-enhanced longitudinal scanline method to extract vehicle trajectories from high-angle traffic cameras. The Dynamic Mode Decomposition (DMD) method is applied to extract vehicle strands by decomposing the Spatial-Temporal Map (STMap) into the sparse foreground and low-rank background. A deep neural network named Res-UNet+ was designed for the semantic segmentation task by adapting two prevalent deep learning architectures. The Res-UNet+ neural networks significantly improve the performance of the STMap-based vehicle detection, and the DMD model provides many interesting insights for understanding the evolution of underlying spatial-temporal structures preserved by STMap. The model outputs were compared with the previous image processing model and mainstream semantic segmentation deep neural networks. After a thorough evaluation, the model is proved to be accurate and robust against many challenging factors. Last but not least, this paper fundamentally addressed many quality issues found in NGSIM trajectory data. The cleaned high-quality trajectory data are published to support future theoretical and modeling research on traffic flow and microscopic vehicle control. This method is a reliable solution for video-based trajectory extraction and has wide applicability.