Pedestrian Trajectory Prediction Technology Combined With Attention Mechanism In Monitoring Scene

With the development of science and technology,there are more and more intelligent systems in the monitoring scene.The ability of these systems to perceive,understand and predict human movement has become more and more important.Pedestrian trajectory prediction technology came into being under this background.It is widely used in the fields of automatic driving,service robot path planning and smart city monitoring system.The traditional pedestrian trajectory prediction research usually adopts the method of manually defining the feature function.However,the manually defined features are difficult to simulate the complex pedestrian interaction behavior,high computational complexity and poor universality.In recent years,with the continuous improvement of deep learning technology,the neural network model based on complex data-driven can well solve the above defects.Therefore,this paper adopts the method of neural network model based on complex data-driven.After studying and analyzing the advantages and disadvantages of existing pedestrian trajectory prediction models,two pedestrian trajectory prediction models are proposed.(1)Aiming at the problem that the pedestrian interaction mechanism in the existing model is simple,the importance of each pedestrian can not be determined,and the speed information is not highly utilized in pedestrian interaction,a pedestrian trajectory prediction model based on attention mechanism is proposed.The main frame of the model adopts a conditional generative adversarial network,and uses the explicit speed information as a conditional label to regulate the network generation,which improves the influence of the speed information on the model.The attention mechanism is set in the decoder part of the generator,and different attention weights are assigned to different pedestrians at different times,which enhances the extraction of pedestrian interaction information.The discriminator uses a fully convolutional network to score the predicted trajectories in segments,so that the classification effect of the discriminator is improved.The experimental results on the ETH and UCY datasets show that the model proposed in this paper has an average displacement error of 0.48 m and a final displacement error of0.93 m.(2)Aiming at the lack of information about the time and speed of pedestrian interaction in the model,a pedestrian trajectory prediction model based on graph attention mechanism is proposed.The overall architecture of the model also adopts the conditional generative adversarial network of speed labels.The difference is that a spatial-temporal information fusion module based on the enhanced graph convolution attention mechanism is designed in the generator,which extracts the motion features of pedestrian trajectory sequences and pays attention to the spatial interaction between them.At the same time,the temporal correlation of pedestrian sequences is explicitly encoded.Finally,the trajectory interaction features combined with spatial-temporal information and speed information are decoded to complete trajectory prediction.In addition,considering the shortcomings of the existing evaluation methods,the average times of collisions is used as the evaluation of the reasonableness of the trajectory.Experiments are carried out on the ETH and UCY datasets.The experimental results show that the proposed algorithm can effectively reduce the occurrence of collisions and better complete the pedestrian trajectory prediction.The average displacement error is 0.40 m,and the final displacement error is 0.79 m.