Target tracking method, device, electronic device and storage medium

Abstract

A target tracking method, an apparatus, a device, and a storage medium. The method comprises: obtaining a current frame (S10); obtaining a target position in a previous first preset frame of the current frame, the scale of a target in a previous frame of the current frame, and a filter in the previous frame of the current frame (S11); on the basis of the target position in the previous frame of the current frame, the scale of the target in the previous frame of the current frame, and the filter in the previous frame of the current frame, utilizing a kernel correlation filtering algorithm to determine the target position of the current frame and the scale of the target in the current frame (S12); obtaining the target position in the previous first preset frame of the current frame and the target position of the current frame, and calculating a scale adjustment parameter corresponding to the current frame (S13); estimating the scale of the target in a next frame of the current frame on the basis of the scale of the target in the current frame and the scale adjustment parameter corresponding to the current frame (S14); and on the basis of the scale of the target in the next frame of the current frame, processing the next frame of the current frame until target tracking is ended (S15). By means of the method, target drifting can be avoided when scale change occurs in the target, and tracking efficiency is improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a target tracking method and device, electronic equipment and a storage medium. Background technique [0002] Moving object tracking is to determine the moving object of interest (for example, pedestrians, vehicles) in each frame of pictures in a continuous video sequence. Target tracking has advantages that humans cannot match, such as convenience and real-time performance, so it has a very wide range of applications in fields such as transportation, security monitoring, and unmanned systems. [0003] At present, video target tracking algorithms are mainly divided into matching-based methods, kernel methods, and motion detection methods. The tracking algorithm based on matching mainly contains feature matching and Bayesian tracking; the Mean-shift method is a representative of the kernel method; there are many target tracking algorithms based on detectio...

AI Technical Summary

Problems solved by technology

Based on the above requirements, the tracking algorithm based on nuclear correlation filtering shows high real-time performance and fast tracking ability, and its tracking accuracy is also relatively high, but the tracking algorithm of nuclear correlation filtering is for fixed-scale processing

However, in the process of target tracking, as the tracking target moves, the scale of the target in the image will change greatly, which seriously affects the performance of the tracking algorithm.

If a fixed scale is used to track the target, if the target has a relatively large scale change, the tracking algorithm in the prior art will cause the target to drift. This phenomenon will have a great impact on the tracking performance, and will eventually lead to track target lost

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