Target detection and tracking method based on unmanned aerial vehicle video

Abstract

The invention relates to a target detection and tracking method based on an unmanned aerial vehicle video. The method comprises the following steps: for a given video image sequence, respectively marking three frames of images which are images when starting to read a video as F1, F2 and F3; performing an optical flow method on the F1 and the F2 to obtain an optical flow feature point set P1, copying an image F2, reserving pixel values at positions corresponding to feature points in the P1 in the copied image, setting the pixel values at other positions to be 0, and recording the processed image as G1; in the same way, operating an optical flow method on the F2 and the F3, and obtaining an image G2 through processing in the same way; performing optical flow detection on the images G1 and G2, and screening the extracted optical flow characteristics, including vector size screening and rectangular window screening; taking an output result of the final detection module as an initializationtemplate of the KCF tracker; and using a KCF tracking algorithm to track subsequent video frames. According to the method, the detection precision is improved, and the unmanned aerial vehicle video target automatic detection and tracking effects are realized.

Description

technical field [0001] The invention relates to a target detection and tracking method based on unmanned aerial vehicle video. Background technique [0002] With the development and progress of computer vision technology, the use of drones equipped with camera equipment for target detection and tracking has been widely used in military and civilian fields such as terrain surveying, traffic monitoring, disaster relief, and military frame difference. Therefore, the analysis and processing of video targets captured by drones is an important part of it. [0003] UAVs can fly in various ways, such as vertical takeoff and landing, hovering, horizontal flight, inverted flight, slow cruise and acceleration, etc. Due to the influence of camera movement, the background of the shooting video is constantly changing, and the lens may be blurred , which brings difficulties to target detection and tracking. [0004] The target detection algorithm can detect the area where the target is b...

AI Technical Summary

Problems solved by technology

[0003] UAVs can fly in various ways, such as vertical take-off and landing, hovering, horizontal flight, inverted flight, slow cruise and acceleration, etc. Due to the influence of camera movement, the background of the shooting video is constantly changing, and the lens may be blurred , which brings difficulties to target detection and tracking

[0004] The target detection algorithm can detect the area where the target is based on the picture. Classical detection algorithms such as frame difference method and background difference method are difficult to deal with the problems caused by lens movement and complex background changes. Optical flow detection algorithm can overcome the impact of lens movement. , however, the optical flow method is sensitive to illumination, and will produce large errors when the brightness of the front and rear frame images changes, and it is very difficult to screen out targets from complex backgrounds, and it is difficult to use for long-term continuous tracking

Although some detection algorithms based on deep learning may achieve good detection and even continuous tracking results, they have high requirements for equipment and require large-scale data training in advance to obtain a generalized model

And some algorithms dedicated to tracking, such as KCF, TLD, MeanShift, Kalman filter, etc., can better adapt to changes in the external environment, but when they track video images, they need to manually select the tracking area at the beginning. Cannot achieve automatic effect

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