Rethinking convolutional neural networks for trajectory refinement

Hanbit Yoon, Usman Ali, Joonhee Choi, Eunbyung Park

Abstract: Highlights•Unlike conventional approaches using CNNs for sequence modeling, e.g., 1D convolution or 2D convolution over the heatmap representation, we propose an architecture that applies 2D convolution over raw trajectory coordinates.•With proper padding and kernel sizes, the proposed method performs 1D convolution, 1D dilated convolution, separating coordinates convolution, and mixing features in one forward pass.•We evaluated the proposed approach on the ETH/UCY and Stanford Drone Dataset, and the proposed method improved the performance by a safe margin, setting a new state-of-the-art result on Stanford Drone Dataset.