Target tracking method based on YOLOv3 and DSST algorithm

A target tracking and algorithm technology, applied in the field of video target tracking and target tracking, can solve the problems of unfavorable commercial applications and high hardware performance requirements, and achieve the effect of improving the success rate and real-time performance and improving anti-interference.

Pending Publication Date: 2019-11-22
JIANGXI LIANCHUANG PRECISION ELECTROMECHANICS CO LTD
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This invention needs to use GPU for accelerated target recognition, which requires re

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  • Target tracking method based on YOLOv3 and DSST algorithm
  • Target tracking method based on YOLOv3 and DSST algorithm
  • Target tracking method based on YOLOv3 and DSST algorithm

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[0022] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

[0023] Such as figure 1 The overall flow chart of a target tracking method based on YOLOv3 and DSST algorithm of the present invention shown in the present invention includes the following steps:

[0024] S1. You can use surveillance video frames for annotation, or you can extract pictures from the VOC2007 / VOC2012 / COCO data set and convert the annotations to make a data training set; use the improved YOLOv3 algorithm in the Darknet network structure to train and predict the data set to obtain Target detection model;

[0025] The improved Darknet network structure is like figure 2 As shown, in or...

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Abstract

The invention discloses a target tracking method based on a YOLOv3 algorithm and a DSST algorithm. The method comprises the following steps: training a target detection model, inputting an image, evaluating a position, detecting a target, taking a position model, a scale model, a target position and a target scale as inputs in the step 3) when a next frame of image comes, and repeating the steps 3) to 4) to complete a video target tracking function. The method is beneficial to fully verifying and objectively evaluating the composition factor influence of the overall effect of the method in twostages of the implementation process, and is also convenient to clarify the improvement and reinforcement targets of the target tracking method. The operation amount of a detection algorithm is effectively reduced, so that the operation speed is increased and the hardware requirement is reduced. The anti-interference performance of the tracking algorithm can be improved, and the success rate andreal-time performance of the tracking algorithm are further improved.

Description

technical field [0001] The present invention relates to the field of video target tracking, in particular to a target tracking method using a deep learning YOLOv3 algorithm (deep learning regression detection algorithm) combined with a DSST algorithm (differential scale space tracking algorithm). Background technique [0002] The tracking of moving targets in video has always been one of the most important research directions in the field of computer vision, and it is widely used in security, transportation, military and other research fields. Existing video target tracking methods are roughly divided into two categories: generative models and discriminative models: generative model methods mainly use the learned target model to search image regions and minimize reconstruction errors, typical representatives are Mean-Shift, Kalman filter and Particle filter, etc.; the main idea of ​​the discriminant model method is to regard the tracking problem as a binary classification pr...

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Application Information

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IPC IPC(8): G06T7/246
CPCG06T7/246G06T7/251G06T2207/10016G06T2207/20081G06T2207/20084
Inventor 蔡锦华祝义荣叶德伟徐刚张返立魏钜熔张文娟
Owner JIANGXI LIANCHUANG PRECISION ELECTROMECHANICS CO LTD
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