Video target data association method and device and storage medium

A data association and target data technology, applied in the electronic field, can solve problems such as inability to correctly distinguish between objects and backgrounds, tracking failures, and inaccurate information on target appearance models

Inactive Publication Date: 2019-05-31
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But based on generative association methods, they sometimes cannot distinguish the object from the background well, because the background pixels and other object pixels within the bounding box of the object are inevitably considered as part of the object, thus making the object appearance model Presence of inaccurate information that prevents it from correctly distinguishing between objects and backgrounds, resulting in possible tracking failures

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  • Video target data association method and device and storage medium
  • Video target data association method and device and storage medium
  • Video target data association method and device and storage medium

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Embodiment 1

[0056] Embodiments of the present application provide a data association method, device, and storage medium for video objects, which are used for data association of video multi-object tracking.

[0057] see Figure 1-a , the data association method of the video object mainly includes the following steps:

[0058] 101. Acquire the first image feature of the target object and the second image feature of the observed object;

[0059] The target object is the target object to be tracked in the video for target tracking. The first image feature is an image feature corresponding to the target object.

[0060] The observation object is an environmental object other than the target object in the video for target tracking, and is used for comparative analysis with the target object. The second image feature is an image feature corresponding to the observation object.

[0061] Specifically, in practical applications, there may be multiple observation objects. For example, it is defi...

Embodiment 2

[0086] The embodiment of the present application introduces the data association method of video objects through specific formulas and algorithm flow, specifically including:

[0087] Define some parameter concepts used in the embodiment of this application, including:

[0088] domain rough set

[0089] Neighborhood rough set is a model proposed to solve the problem that classical rough set cannot handle continuous data. The neighborhood of the model is defined according to the maximum distance from the center point of the neighborhood to the boundary on a certain measure, and the attribute is reduced by the dependence of the decision attribute on the condition attribute.

[0090] Suppose the finite non-empty set U={x defined on the real number space 1 ,x 2 ,...,x n}, D is the decision attribute, for any x i ∈U, its δ neighborhood is defined as:

[0091] δ(x i )={x|x∈U,Δ(x,x i )≤δ} (1)

[0092] Among them, Δ(x,x i ) is the distance function, and the distance function...

Embodiment 3

[0225] see figure 2 , an electronic device is provided for the embodiment of the present application. The electronic device can be used to achieve the above Figure 1-a The data association method of the video object provided by the illustrated embodiment. Such as figure 2 As shown, the electronic device mainly includes:

[0226] An acquisition unit 210, configured to acquire the first image feature of the target object and the second image feature of the observed object;

[0227] A similarity calculation unit 220, configured to perform feature similarity calculations of N types of feature categories on the first image feature and the second image feature to obtain N sets of feature similarity results, where N is an integer greater than 1;

[0228] The feature fusion unit 230 is configured to screen the N groups of feature similarity results based on the feature similarity of the rough set, and fuse the screened results to obtain a feature fusion result;

[0229] An asso...

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Abstract

The invention discloses a video target data association method and device and a storage medium. The data association method of the video target comprises the steps of obtaining a first image feature of a target object and a second image feature of an observation object; performing feature similarity calculation of N feature categories on the first image feature and the second image feature; screening the N groups of feature similarity results based on the feature similarity of the rough set, and fusing the screened results to obtain a feature fusion result; based on maximum entropy intuitionistic fuzzy clustering, performing correlation cost matrix calculation according to the feature fusion result to obtain a correlation cost matrix calculation result; judging whether the target object isassociated with the observation object or not according to an association cost matrix calculation result; if not, performing target track management on the target object, and counting a new startingtrack and a new ending track of the target object in the video to obtain the ending track set and the new track set; and performing fuzzy trajectory association according to the final trajectory set and the new trajectory set.

Description

technical field [0001] The present application relates to the field of electronic technology, and in particular to a data association method, device and storage medium of video objects. Background technique [0002] Video multi-target tracking is one of the important research contents of machine vision, which mainly obtains basic motion information such as position, attitude and trajectory of moving targets in video. With the development of digital computing technology, video multi-target tracking has opened up multiple research fields and application fields, involving intelligent video surveillance, virtual reality, human-computer interaction, automatic driving, traffic control, oceanography, intelligent robots, remote sensing , biomedicine and other fields have attracted more and more scholars and researchers to actively participate and achieved a large number of research results. However, in the environment of complex background and densely occluded targets, there are ma...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06T7/246G06T7/41G06K9/46
Inventor 李良群湛西羊谢维信刘宗香
Owner SHENZHEN UNIV
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