A Time Sequence Images Matching Method Based on the Siamese Network

The similar analysis of time sequence images to achieve image matching is a foundation of tasks in dynamic environments, such as multi-object tracking and dynamic gesture recognition. Therefore, we propose a matching method of time sequence images based on the Siamese network. Inspired by comparative learning, two different comparative parts are designed and embedded in the network. The first part makes a comparison between the input image pairs to generate the correlation matrix. The second part compares the correlation matrix, which is the output of the first comparison part, with a template, in order to calculate the similarity. The improved loss function is used to constrain the image matching and similarity calculation. After experimental verification, we found that it not only performs better, but also has some ability to estimate the camera pose.

Automated summary

The proposed method utilizes a Siamese network for image matching in time sequence, with two comparison parts that generate correlation matrix for similarity calculation, and an improved loss function for constraint. It shows better performance and some capability in estimating camera pose.