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Automatic target tracking method for aerially photographed videos

A target tracking and aerial photography technology, applied in the field of aerial video target tracking, can solve problems such as tracking errors, achieve the effect of reducing the tracking error rate and overcoming the problem of spatial clustering errors

Inactive Publication Date: 2013-11-27
NORTHWESTERN POLYTECHNICAL UNIV
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AI Technical Summary

Problems solved by technology

However, the tracking result of this method depends heavily on the registration accuracy of adjacent frames and the target detection accuracy. Once a missed detection occurs or the distance between targets is too close, tracking errors will occur, and the tracking error rate is about 18% on average.

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  • Automatic target tracking method for aerially photographed videos
  • Automatic target tracking method for aerially photographed videos
  • Automatic target tracking method for aerially photographed videos

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Embodiment Construction

[0039] The specific steps of the aerial video automatic target tracking method of the present invention are as follows:

[0040]Step 1. Use the pyramid optical flow algorithm to calculate the optical flow vector features for the existing optical flow feature points and the newly detected Harris corner points, and calculate the affine transformation model between adjacent frames and the motion state of the feature points through the RANSAC method. Using motion constraints, space position constraints, and time constraints to manage feature points, the most reliable optical flow trajectory is obtained, and the optical flow trajectory with a length greater than N is selected.

[0041] To input an aerial video sequence, the first step is to extract the optical flow trajectory features and manage them. ObjNum indicates the number of targets currently being tracked, and the initial value is 0. L represents the number of all current effective optical flow trajectories, and the set of...

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Abstract

The invention discloses an automatic target tracking method for aerially photographed videos and aims to solve the technical problem that an existing aerially photographed video target tracking method based on stable image detection and data association is high in tracking error rate. According to the technical scheme, firstly, a pyramid optical flow method is used for extracting robust optical flow track features rather than simple motion foreground segmentation; secondly, motion constraint, position constraint and existence duration constraint are utilized for effective management of an optical flow track; lastly, clustering in time dimension is conducted according to a spatial clustering result of a plurality of continuous frames to effectively solve the problem of spatial clustering errors under the conditions of target intersection and a close range between targets. According to the automatic target tracking method for the aerially photographed videos, a target tracking result is obtained on the basis of spatial clustering, and therefore the tracking error rate is lowered. Tests prove that the tracking error rate is lowered by 10% from 18% in the background art to 8%.

Description

technical field [0001] The invention relates to an aerial video target tracking method, in particular to an aerial video automatic target tracking method. Background technique [0002] Automatic object tracking in aerial video is an important research topic in the field of computer vision. The existing automatic target tracking methods for aerial video are mainly based on the framework of first image stabilization and then detection and tracking. [0003] The document "Moving Objects Detection and Tracking Framework for UAV-based Surveillance, Fourth Pacific-Rim Symposium on Image and Video Technology, 2010: 456-461" discloses an aerial video target tracking algorithm based on image stabilization detection and data association. The method first realizes the registration between adjacent frames through SIFT feature point matching, then performs background suppression through the mixed Gaussian background modeling method, uses the Graph-Cut method to accurately segment the ta...

Claims

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Application Information

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IPC IPC(8): G06T7/20
Inventor 张艳宁杨涛仝小敏马文广
Owner NORTHWESTERN POLYTECHNICAL UNIV
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