Crowd trajectory prediction method based on multi-precision interaction

Abstract

The invention provides a crowd trajectory prediction method based on multi-precision interaction, and belongs to the technical field of computer vision. The method comprises the following steps that: historical states of pedestrians are coded by using an LSTM network; a global interaction modeling mode and a local interaction modeling mode are adopted for motion interaction of the pedestrians, during global interaction modeling, scenes are firstly divided, regional modeling is carried out on each divided sub-region, then global interaction information is obtained through integration, and finally the local interaction information and the global interaction information are spliced to obtain complete interaction information; and decoding is preformed at a decoding end by using the LSTM network to predict pedestrian trajectories. The trajectories of the pedestrians are finely adjusted through the global interaction information, the detailed motion information of the pedestrians can be captured through the local interaction information, and the pedestrians can timely avoid recent motion, so that the trajectories are closer to real trajectories. By adopting the method, the pedestrian trajectories can be predicted more accurately, and the calculation complexity is reduced while the anisotropy of the interaction information is ensured.

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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