Pedestrian trajectory prediction based on temporal attention

Abstract

Aiming at the problem that pedestrian trajectory prediction network based on Encoder-Decoder structure is easy to lose part of trajectory information in the process of long sequence encoding and decoding, Generative Adversarial Network based on Temporal Attention (TA) is proposed in this paper. It is used to assign influence weights to the trajectory information of the encoding and decoding layer, so that the model can make full use of the trajectory information useful for predicting the trajectory in the future and reduce the influence of redundant information. This paper adds a TA to the encoding and decoding layers of the Generative Adversarial Network, and trains it on the ETH and UCY datasets. Experimental results show that the proposed network has better prediction accuracy than existing methods.