Multi-information-based Convolutional Neural Network with Attention Mechanism for Pedestrian Trajectory Prediction

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.