Twin candidate region generation network target tracking method based on attention mechanism

Abstract

The invention relates to a twin candidate region generation network target tracking method based on an attention mechanism, and belongs to the technical field of image processing. The twin candidate region generation network target tracking method comprises the following specific steps: 1, extracting initial target template features and target search region features by using a twin network; 2, constructing a spatial attention network to enhance a target template foreground and suppress a semantic background; 3, constructing a channel attention network to activate strong correlation characteristics of the target template, and eliminating redundancy; and 4, constructing a candidate region generation network to realize multi-scale target tracking. The twin candidate region generation networktarget tracking method has the advantages that the attention mechanism is used for constructing the adaptive target appearance feature model; the target foreground is enhanced; the semantic backgroundis inhibited; and the difference features of the target foreground and the interference background are highlighted; redundant information is removed, so that the efficient appearance feature expression capacity is obtained, and the target drifting problem is effectively relieved.

Description

technical field [0001] The invention relates to a network target tracking method for generating twin candidate regions based on an attention mechanism, and belongs to the technical field of image processing. Background technique [0002] With the improvement of computer hardware capabilities and the advent of the era of artificial intelligence, deep neural networks are widely used in the field of target tracking due to their efficient data computing capabilities and feature extraction capabilities. The tracking method based on the convolutional neural network can significantly improve the accuracy and success rate of the tracking results, showing the good performance of the target tracking method. However, the online update process and candidate sample generation process of convolutional neural network consume a lot of time, which greatly limits the running speed of tracking methods. In order to achieve real-time tracking speed, the twin network abandons the online update p...

AI Technical Summary

Problems solved by technology

However, the tracking method based on the Siamese candidate region generation network is weak in discriminating the target foreground and the semantic interference background, and it is easy to cause the tracking results to deviate from the real target quickly, resulting in the problem of target drift.

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