DSTCNN: Deformable spatial-temporal convolutional neural network for pedestrian trajectory prediction

Abstract

Pedestrian trajectory prediction holds significant research value in service robots, autonomous driving, and intelligent monitoring. Currently, most pedestrian trajectory prediction methods focus on data-driven models based on recurrent neural networks, but there is insufficient research on data-driven models based on convolutional neural networks. In this study, we first analyze the two problems in pedestrian trajectory prediction methods based on convolutional neural networks: 1. Previous trajectory prediction methods based on convolutional neural networks have spatial-temporal entanglement problems; 2. These methods are limited by their fixed convolution kernels and cannot accurately model social and temporal interactions. Furthermore, we propose a deformable spatial-temporal convolutional neural network (DSTCNN) to better adapt to the pedestrian trajectory prediction task. The deformable spatial-temporal convolutional neural network models spatial and temporal interactions separately, overcoming the shortcomings of spatial-temporal entanglement. The deformable spatial-temporal convolution also gets rid of the fixed convolution kernel, making the modeling of spatial-temporal interactions more accurate. On the ETH and UCY datasets, the average displacement error and final displacement error of our method are 0.29 and 0.53 meters, respectively. In kernel density estimation, average Mahalanobis distance, and average maximum eigenvalue metrics, our method still achieves better performance compared to baseline methods. Moreover, the deformable spatial-temporal convolutional neural network is a memory-efficient model with only 4.1K parameters.