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Multi-target tracking method and device based on offline MBM filter

A multi-target tracking and multi-target technology, which is applied in the field of multi-target tracking based on offline MBM filter, can solve the problems of track termination lag, many false targets, and output track interruption, so as to avoid false targets and improve tracking performance. , the effect of reducing the amount of calculation

Pending Publication Date: 2022-08-05
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0004] However, the standard MBM filter has problems such as output track interruption, track termination lag, and many false targets in low detection probability scenarios.

Method used

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  • Multi-target tracking method and device based on offline MBM filter
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  • Multi-target tracking method and device based on offline MBM filter

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Embodiment Construction

[0062] In order to make the objectives, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

[0063] In one embodiment, as figure 1 As shown, a multi-target tracking method based on an offline MBM filter is provided, including the following steps:

[0064] Step S1, according to the multi-target state of the multi-target to be tracked at the previous moment, obtain the predicted multi-target state at the current moment:

[0065] It can be understood that, before performing step S1, it is also necessary to initialize the offline MBM filter, that is, set the posterior MBM density at the initial moment to 0;

[0066] It can be understood that the multi-target state of the ...

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Abstract

The invention relates to a multi-target tracking method and device based on an offline MBM filter. The method comprises the steps of obtaining a predicted multi-target state of a current moment according to a multi-target state of multiple targets to be tracked at a previous moment, obtaining a corrected multi-target state of the current moment according to the predicted multi-target state of the current moment, and obtaining a corrected multi-target state of the current moment according to the corrected multi-target state of the current moment. And performing Bayesian recursive filtering to obtain multi-target state estimation of the to-be-tracked multiple targets at the current moment, circulating the steps until Bayesian recursive filtering is completed to obtain a multi-target state estimation set, determining the authenticity of each to-be-tracked multiple target in the multi-target state estimation set in an off-line manner through significance testing, and outputting the estimation of the multi-target state at each moment. By adopting the method, the target can be prevented from being lost due to continuous multi-frame leak detection, false targets caused by clutters are avoided, and the multi-target tracking performance in a low-detection probability scene is effectively improved.

Description

technical field [0001] The present application relates to the technical field of multi-target tracking, and in particular, to a multi-target tracking method and device based on an offline MBM filter. Background technique [0002] The purpose of Multi-Target Tracking (MTT) is to estimate the number and motion states of targets in a scene from a series of imperfect sensor measurements, and even give the trajectory of each target. After more than half a century of development, multi-target tracking technology has been widely used in civil and military fields, including navigation and surveillance, remote sensing, air traffic control, computer vision, and more. Traditional multi-target tracking algorithms based on data association, such as Joint Probability Data Association (JPDA) filter and Multiple Hypothesis Tracking (MHT) algorithm, face combinatorial explosion problems and complex scenes in dense clutter environments track down performance degradation issues. [0003] The...

Claims

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Application Information

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IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 王森鲍庆龙潘嘉蒙戴华骅唐泽家苏汉宁李水晶涛
Owner NAT UNIV OF DEFENSE TECH
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