A Crowd Trajectory Prediction Method Based on Multi-precision Interaction

Abstract

The invention proposes a crowd trajectory prediction method based on multi-precision interaction, which belongs to the technical field of computer vision. The method of the present invention includes: using LSTM network to encode the historical state of pedestrians; adopting global interactive modeling and local interactive modeling methods for pedestrian motion interaction, firstly dividing the scene when global interactive modeling, and performing regional analysis on each divided sub-area Modeling, and then integrating to obtain global interaction information, and finally splicing local interaction information and global interaction information to obtain complete interaction information; at the decoding end, an LSTM network is used to decode and predict pedestrian trajectories. The trajectory of pedestrians can be fine-tuned through the global interaction information, and the detailed motion information of pedestrians can be captured through the local interaction information. Pedestrians can make timely avoidance of recent movements and get closer to the real trajectory. By adopting the method of the invention, the trajectory of pedestrians can be predicted more accurately, and the computational complexity is reduced while ensuring the heterogeneity of mutual information.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and relates to crowd trajectory prediction and crowd interaction modeling technology, in particular to a crowd trajectory prediction method based on multi-precision interaction. Background technique [0002] The trajectory prediction algorithm realizes the prediction of the target's future trajectory information based on the historical position information of pedestrians, which is one of the research hotspots in the field of computer vision at present. With the development of 5G network commercialization and urban modernization, the country attaches great importance to the construction of key directions such as smart transportation and public safety. As a necessary basic research technology, trajectory prediction algorithms are used in real-life scenarios such as autonomous driving, robot navigation, and pedestrian intention analysis. play a key role in. For example, in a robot navigatio...

AI Technical Summary

Problems solved by technology

[0011] In view of the large number of pedestrians in complex scenes, the complexity of calculating pedestrian interactions based on the attention mechanism is high, and the modeling accuracy of the overall interaction modeling for the global scene is poor, and the interactions of different pedestrians lack heterogeneity. The present invention proposes A crowd trajectory prediction method based on multi-precision interaction, which makes pedestrian interaction modeling more detailed and reasonable through the interactive modeling methods of Global Interaction Module (GIM) and Local Interaction Module (LIM) , to achieve the effect of improving the accuracy of crowd trajectory prediction, and has the advantage of reducing the number of calculations while ensuring the personalized interaction of pedestrians

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