YOLO-based specific target tracking method

Abstract

The invention provides a YOLO-based specific target tracking method, and belongs to the technical field of target tracking. The method comprises the following steps: firstly, setting a to-be-tracked specific target, shooting a video in real time by using a camera, sending the video to a computer, and putting an image of the specific target as a template into a template folder; Taking the first frame of image with the specific target in the video as a first frame of image, and carrying out target detection on each frame of image by using a YOLO detection algorithm to obtain a detection result corresponding to the type of the object where the specific target is located in each frame of image; And correcting the detection result by using a Deepsort algorithm, and then tracking a specific target by using a Surf algorithm on the corrected result. According to the method, continuous tracking of a single specific target can be realized, the speed is high, the real-time performance is good, and the method is well suitable for environment changes.

Description

technical field [0001] The invention relates to the technical field of target tracking, in particular to a specific target tracking method based on YOLO. Background technique [0002] At present, with the development of computer vision technology, vision-based moving target detection and tracking has become a current research hotspot, and has broad application prospects in video surveillance, virtual reality, human-computer interaction, planetary detection, and precision guidance. [0003] YOLO is an object detection algorithm based on deep learning neural network architecture. The algorithm divides the input image into 7 by 7 grids, and presets 5 default boxes centered on each grid. The output of the algorithm is in The offset based on the 5 default boxes is predicted at each grid, and the corresponding category is predicted at the same time. The 5 preset default boxes are 5 representative boxes obtained by clustering on a large number of object detection data sets, thus e...

AI Technical Summary

Problems solved by technology

[0004] Most of the existing YOLO-based target tracking methods cannot track a specific single target, but can only track the same type of object, and cannot distinguish between different individuals in the same type of object, and the existing YOLO-based target Tracking methods also do not account for occluded objects

For example, in 2018, Zhu Chenyang from Nanjing University of Science and Technology proposed a YOLO3-based automatic face tracking camera robot system research. They designed an automatic tracking camera robot and proposed a deep convolutional neural network based on

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