基于 Transformer 动态场景信息生成对抗网络的行人轨迹预测方法

Pedestrian trajectory prediction is an important part of video surveillance. The current methods are not accurate and sometimes violate common senses because scene information is not fully used. To eliminate the above shortcomings, this paper proposes a transformer generated adversarial network(GAN) algorithm which combines dynamic scene information with pedestrian social interaction information. The convolution neural network model of the dynamic scene extraction module is utilized to extract the dynamic scene information features of the target pedestrian, and the encoder in the generator network uses transformer to model the features of social interaction information and trajectory information of pedestrians. Experimental results on ETH and UCY datasets show that, compared with social GAN model, our method improves the accuracy of average displacement error by 25.61% and the accuracy of average final displacement error by 38.44% in multiple scenarios.