Trajectory semantic segmentation method based on dynamic pedestrian agent hybrid model

Abstract

The invention provides a trajectory semantic segmentation method based on dynamic pedestrian agent hybrid model. The main content comprises: a dynamic pedestrian agent hybrid model (MDA), a hidden Markov model (HMM) and trajectory semantic segmentation. The process is as follows: the MDA is a layered Bayesian model, and the model predicts the dynamics and reliability parameter of each agent through a modified Kalman filter (used for processing an observed value of trajectory missing trace) and an iterative expectation maximum algorithm (EM algorithm); then the trajectories are gathered according to the estimated agent; then, the trajectory is transmitted to the HMM, and the parameter phi is estimated; and finally, semantic segmentation is performed on the trajectory in the HMM by using aviterbi algorithm and the learned HMM parameter phi. According to the trajectory semantic segmentation method provided by the invention, the behavior of a pedestrian can be analyzed during the trajectory segmentation of the pedestrian, and corresponding estimation is performed, so that the accuracy and the efficiency are higher.

Description

technical field [0001] The invention relates to the field of trajectory semantic segmentation, in particular to a trajectory semantic segmentation method based on a dynamic pedestrian-agent hybrid model. Background technique [0002] With the development of computer vision, trajectory semantic segmentation technology has been gradually improved. Trajectory semantic segmentation is an important branch in the field of artificial intelligence and an important part of image understanding in machine vision technology. It has attracted people's attention because of its wide range of applications. In pedestrian traffic, trajectory semantic segmentation technology can be used to identify pedestrian trajectory rules, and formulate relevant traffic regulations according to the rules to reduce the occurrence of traffic accidents. In terms of criminal investigation, trajectory semantic segmentation technology can be used to estimate the suspect's trajectory to determine its location. ...

AI Technical Summary

Problems solved by technology

[0004] For the problem of low efficiency and accuracy, the object of the present invention is to provide a trajectory semantic segmentation method based on a dynamic pedestrian-agent hybrid model, MDA is a layered Bayesian model, and this model is passed through a modified Kalman filter (for handling missing observations of trajectories) and an iterative maximum expectation algorithm (EM algorithm) to estimate the dynamic and reliability parameters of each agent; then gather trajectories according to the estimated agents; then, the trajectories will be transmitted to In the HMM and estimate it about the parameter Θ

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