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 4.1 parameters.

中文翻译：

---

DSTCNN：用于行人轨迹预测的可变形时空卷积神经网络

行人轨迹预测在服务机器人、自动驾驶、智能监控等领域具有重要的研究价值。目前，大多数行人轨迹预测方法集中在基于循环神经网络的数据驱动模型，但基于卷积神经网络的数据驱动模型研究不足。在本研究中，我们首先分析基于卷积神经网络的行人轨迹预测方法中的两个问题：1.以往基于卷积神经网络的轨迹预测方法存在时空纠缠问题； 2.这些方法受到固定卷积核的限制，无法准确地模拟社交和时间交互。此外，我们提出了一种可变形时空卷积神经网络（DSTCNN）以更好地适应行人轨迹预测任务。可变形时空卷积神经网络分别对时空交互进行建模，克服了时空纠缠的缺点。可变形时空卷积也摆脱了固定卷积核，使得时空交互的建模更加准确。在ETH和UCY数据集上，我们方法的平均位移误差和最终位移误差分别为0.29和0.53米。在核密度估计、平均马氏距离和平均最大特征值度量中，与基线方法相比，我们的方法仍然取得了更好的性能。而且，可变形时空卷积神经网络是一种内存高效的模型，参数只有 4.1 个。