Predicting pedestrian trajectory is useful in many applications, such as autonomous driving and unmanned vehicles. However, it is a challenging task because of the complexity of the interactions among pedestrians and the environment. Most existing works employ long short-term memory networks to learn pedestrian behaviors, but their prediction accuracy is not good, and their computing speed is relatively slow. To tackle this problem, we propose a multi-information-based convolutional neural network (MI-CNN) to incorporate the historical trajectory, depth map, pose, and 2D-3D size information to predict the future trajectory of the pedestrian subject. After training, we evaluate our model on crowded videos in the public datasets MOT16 and MOT20. Experiments demonstrate that the proposed method outperforms state-of-the-art approaches both in prediction accuracy and computing speed.

预测行人的轨迹在许多应用中很有用，例如自动驾驶和无人驾驶汽车。然而，由于行人与环境之间相互作用的复杂性，这是一项具有挑战性的任务。现有的大多数作品都采用长短期记忆网络来学习行人的行为，但是其预测精度不高，计算速度也相对较慢。为了解决这个问题，我们提出了一个基于多信息的卷积神经网络（MI-CNN），以结合历史轨迹，深度图，姿态和2D-3D大小信息来预测行人主体的未来轨迹。训练后，我们在公共数据集MOT16和MOT20中的拥挤视频上评估我们的模型。