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A Ship Trajectory Prediction Method Based on Image Superposition

A prediction method and ship trajectory technology, applied in image analysis, image enhancement, graphics and image conversion, etc., can solve the problems of unsatisfactory accuracy and complicated calculation process, and achieve good prediction effect and comprehensive trajectory information

Active Publication Date: 2021-06-01
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0003] The existing technology is to extract target features on the sequence diagram of each frame for trajectory tracking and prediction, but the current accuracy is not ideal and the calculation process is complicated

Method used

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  • A Ship Trajectory Prediction Method Based on Image Superposition
  • A Ship Trajectory Prediction Method Based on Image Superposition
  • A Ship Trajectory Prediction Method Based on Image Superposition

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Embodiment Construction

[0051] Such as figure 1 As shown, a ship track prediction method based on image superposition, the method specifically includes the following steps:

[0052] Step 1: extract the sequence diagram of each frame from the video, and preprocess the image at the same time. Use the method of histogram equalization to reduce the background gray value of the image to highlight the characteristics of the hull.

[0053] Step 2. Select n time periods, let the time in each time period be Δt, select m frames of images from the Δt time period, and record it as frame 1 , frame 2 ,...frame m .

[0054] Step 3. Select the image of the current time period and record it as frame s , where s=1, 2, ..., m.

[0055] calculation frame s The grayscale histogram of , find all ship targets, a total of m, mark all ships with the circumscribed rectangle, take the length and width of the image as the coordinate axis, and the upper left corner of the circumscribed rectangle (x 1s ,y 1s ), the lower...

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Abstract

The invention discloses a ship track prediction method based on image superposition. This method first uses a video to shoot the sea voyage of the ship, extracts the sequence diagram of each frame in the video, selects a time period, superimposes and compresses the images of each frame in this time period into one image, and then The image is processed, the feature information is extracted, the trajectory information is repeated many times. Then each compressed image is used as a new trajectory data set, and then predicted by Kalman filter. The present invention superimposes the sequence diagrams in each period of time, and extracts the target contour features, so that the target point of each frame becomes the target trajectory of each period of time, so that when the Kalman filter method is used for trajectory prediction, The trajectory information is more comprehensive and the prediction effect is better.

Description

technical field [0001] The invention relates to the field of trajectory prediction, in particular to a ship trajectory prediction method based on image superposition. Background technique [0002] Relying on modern computer networks and communication equipment, using advanced intelligent processing algorithms, and combining quantitative and qualitative analysis modes to establish ship trajectory prediction models, this is one of the important contents of modern maritime digital information construction. And with the continuous development of artificial intelligence, unmanned ships at sea will definitely become a hot issue in the future, so the trajectory prediction of ships will also be an important means of future research on unmanned ships. [0003] The existing technology is to extract target features on the sequence diagram of each frame for trajectory tracking and prediction, but the current accuracy is not ideal and the calculation process is complicated. Contents of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/277G06T7/246G06T3/40G06T5/40G06T5/50
CPCG06T3/40G06T5/40G06T5/50G06T2207/10016G06T2207/20024G06T7/246G06T7/277
Inventor 侯志鹏陈张平周杰孔亚广张扬
Owner HANGZHOU DIANZI UNIV
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