Tracking Objects as Pixel-wise Distributions

Multi-object tracking (MOT) requires detecting and associating objects through frames. Unlike tracking via detected bounding boxes or tracking objects as points, we propose tracking objects as pixel-wise distributions. We instantiate this idea on a transformer-based architecture, P3AFormer, with pixel-wise propagation, prediction, and association. P3AFormer propagates pixel-wise features guided by flow information to pass messages between frames. Furthermore, P3AFormer adopts a meta-architecture to produce multi-scale object feature maps. During inference, a pixel-wise association procedure is proposed to recover object connections through frames based on the pixel-wise prediction. P3AFormer yields 81.2\% in terms of MOTA on the MOT17 benchmark -- the first among all transformer networks to reach 80\% MOTA in literature. P3AFormer also outperforms state-of-the-arts on the MOT20 and KITTI benchmarks.Comment: Accepted in ECCV22 as an oral presentation paper. The code&project page is at https://github.com/dvlab-research/ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distribution

Numerical investigation on the aerodynamics of a simplified high-speed train under crosswinds

The yaw effect of the side flow around a high-speed train is studied by means of large eddy simulation at two typical yaw angles of φ=30° and 60°, respectively. Both the mean and fluctuating values of lift force and side force coefficients increase obviously as the yaw angle increases. The spectral analysis indicates that the time-dependent aerodynamic forces are dominated by several energetic frequencies and the frequency range is broadened to a higher extent for the large yaw angle. To have a better understanding of the train aerodynamic behaviors, the dedicate three-dimensional vortical structures are analyzed for the flow at the two yaw angles. Moreover, the time-averaged flow patterns, turbulent statistics and the surface forces are also studied on sectional planes along the train