Depth correlation target tracking algorithm based on mutual reinforcement and multi-attention mechanism learning

Abstract

The invention discloses a depth correlation target tracking algorithm based on mutual reinforcement and multi-attention mechanism learning. The algorithm comprises the following steps: inputting a previous frame of target area and a next frame of search area; initializing parameters of the feature extractor, learning the parameters of the feature extractor through gradient descent through mean square error loss, and extracting features from the search area through the feature extractor; calculating autocorrelation of the features, and learning a filter template through a ridge regression closed solution; enabling the next frame to determine a search area through a target position speculated by the previous frame, carrying out feature extraction through a designed feature extractor, and calculating cross-correlation of features of the target area and the search area; carrying out related operation on the characteristics and a filter template, wherein the maximum value in the output values is the latest position of target tracking; and each frame learns and updates the target filter template.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a depth-related target tracking algorithm based on mutual reinforcement and multi-attention mechanism learning. Background technique [0002] Object tracking is one of the core issues in the field of computer vision and has a wide range of applications, such as human motion analysis, video surveillance, and automatic driving. Although a large number of tracking algorithms have been proposed for various scenarios, robust visual tracking systems are still elusive due to factors such as deformation, occlusion, illumination changes, background clutter, and fast motion. [0003] In recent years, many target tracking algorithms based on correlation and depth features have emerged, which can perform better video single target tracking. The representative one is the deep correlation tracking algorithm of mutual reinforcement and multi-attention mechanism learning. However, th...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of insufficient expressive power of a single low-level feature and redundant information in the current tracking algorithm, the present invention proposes a depth-related target tracking algorithm based on mutual reinforcement and multi-attention mechanism learning

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