Target tracking method and system of full convolution twin network based on multi-layer feature fusion

Abstract

The invention discloses a target tracking method and system of a convolution twin network based on multi-layer feature fusion. The method comprises the following steps of: according to the target position and size of the images, cutting out the target template images and the search area images of all images in the image sequence training set, and forming a training data set by image pairs composedof the target template images and the search area images; constructing a convolution twin network based on multi-layer feature fusion; training the convolution twin network based on the multi-layer feature fusion based on the training data set to obtain a trained convolution twin network based on the multi-layer feature fusion; using the trained convolution twin network based on multi-layer feature fusion for target tracking. In the process of tracking targets, the invention integrates scores of different layers, combines high-level semantic features and bottom-level detail features, better distinguishes the interference of similar or similar targets, and prevents the problems of target drift and target loss in the tracking process.

Description

technical field [0001] The invention belongs to the cross field of digital image processing, deep learning and pattern recognition, and more specifically, relates to a target tracking method and system of a convolution twin network based on multi-layer feature fusion. Background technique [0002] Target tracking plays a very important role in computer vision. However, due to the complexity of natural scenes, the sensitivity of targets to illumination changes, the requirements of tracking for real-time and robustness, and the existence of factors such as occlusion, attitude and scale changes, etc. , making tracking the problem still difficult. The traditional target tracking method cannot extract rich features for the target, so that the target and the background are strictly distinguished, and it is prone to tracking drift, so it cannot track the target for a long time. With the rise of deep learning, the general convolutional neural network can effectively extract rich fe...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the defects of the prior art, the purpose of the present invention is to solve the technical problems of tracking drift and target loss caused by similar background interference in the prior art

Images