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Aerial video moving object detection method based on multiple model estimation

A technology of moving targets and detection methods, applied in computing, image data processing, instruments, etc., can solve the problems that detection results depend on scene complexity, false alarms, etc.

Active Publication Date: 2014-12-17
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

However, the detection results of this method depend heavily on the complexity of the scene. Once there are static objects such as buildings above the ground, utility poles, and viaducts in the scene that are not in the background plane, false alarms will occur, and the average detection error rate is about 10%

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  • Aerial video moving object detection method based on multiple model estimation
  • Aerial video moving object detection method based on multiple model estimation
  • Aerial video moving object detection method based on multiple model estimation

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Embodiment Construction

[0040] Now in conjunction with embodiment the present invention will be further described:

[0041] 1. Mean shift color segmentation

[0042]To input an aerial video sequence, the first step is to use the pyramid Mean shift method to color-segment the current frame image. In order to ensure calculation efficiency and accuracy at the same time, in this embodiment, the number of pyramid layers is 3, the color window width is 10, and the spatial position window width is 10. Color blocks with an area greater than the given threshold Thresh are taken as background blocks, and the rest are foreground blocks. In this embodiment, Thresh=0.01×ImgWidth×ImgHeight is taken, and ImgWidth and ImgHeight represent the width and height of the input video image respectively. {PatchB i |i=1,2,...,BNum} indicates the background block in the current frame segmentation result, where BNum indicates the number of background blocks in the frame segmentation result, the i-th block PatchB i ={Area i...

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Abstract

The invention relates to an aerial video moving object detection method based on multiple model estimation. The aerial photography video moving object detection method comprises the following steps: firstly, utilizing a Mean shift color segmentation method to segment a scene into a plurality of color blocks; then, utilizing dense pyramid luminous flux characteristics, and adopting a RANSAC (Random Sample Consensus) method to calculate an affine transformation model corresponding the color blocks with a large area; managing smaller color blocks, and analyzing point movement consistency in the blocks so as to carry out multiple model membership degree calculation, wherein the color blocks with the big membership degree are moving blocks, and otherwise, the color blocks are false alarm targets; and finally, combining and denoising the moving blocks, and outputting and displaying a detection result. The invention obtains a 5% detection error rate of a test in a public aerial video database, and the error rate is lowered by 5% than a traditional 10% error rate.

Description

technical field [0001] The invention relates to a moving target detection technology, in particular to a method for detecting a moving target in an aerial video based on multi-model estimation. Background technique [0002] Aerial video moving object detection is an important research topic in the field of computer vision. Existing methods for moving object detection in aerial video are mainly based on a moving object detection framework estimated by a single background model. The document "Moving object detection in aerial video based on spatiotemporal saliency, Chinese Journal of Aeronautics, 2013, 26(5): 1211-1217" proposes an aerial video moving target detection algorithm based on spatiotemporal saliency analysis. This method firstly obtains the salient area in the time dimension by estimating the background model and frame difference, which is the candidate area for rough extraction, then performs saliency analysis in the space dimension to obtain the appearance detail...

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
Owner NORTHWESTERN POLYTECHNICAL UNIV
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