Multi-target tracking positioning and motion state estimation method based on unmanned aerial vehicle

Abstract

The invention discloses a multi-target tracking positioning and motion state estimation method based on an unmanned aerial vehicle, and the method comprises the specific steps: inputting target observation image data under the aerial photographing of the unmanned aerial vehicle at a high-altitude visual angle, and obtaining a target detection result; predicting target trajectory parameters by using Kalman filtering, then carrying out cascade matching by using a Hungary algorithm, carrying out IoU matching on a cascade matching result, updating the state of a target trajectory by using Kalman filtering, updating each successfully matched trajectory by using a corresponding detection result, and processing the unmatched trajectories and unmatched detection results; and transplanting the result into an ROS environment, converting pixel coordinates on a two-dimensional image shot by a camera of the unmanned aerial vehicle into space coordinates of the real world, and calculating the moving speed of the target. According to the method, migration adaptation of a multi-target tracking and positioning algorithm is completed in the operation process of the high-altitude unmanned aerial vehicle, and accurate calculation of the multi-target motion state is achieved.

Description

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AI Technical Summary

Benefits of technology

This technology allows for precise navigation over long distances by creating an avatar's visual representation based upon data from multiple cameras mounted at different locations around it. It uses advanced techniques like image processing algorithms or machine learning methods to improve its performance. Overall, this technology helps navigate through space without being obstructive even when there are many objects within sight (such as buildings).

Problems solved by technology

Technological Problem addressed in this patented technical solution describes how complex object detection systems like Multi Object Tracking (MOFT), which involves multiple objects being tracked together over different terrains or areas covered by obstacles during flight. These challenges include environmental disturbances caused by sunlight glare, shading, movement within buildings, and other sources affecting visual quality. Current algorithms have limitations including slow processing times due to large computational costs associated with these techniques.

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