Abstract
In the security video surveillance system, image splicing technology faces the difficulty of achieving large parallax and small overlap area. This paper summarizes the traditional stitching technology and analyzes that the image matching algorithm is the key technology to solve the problem of large parallax stitching. Aiming at the problems that traditional matching methods with low matching accuracy cannot adapt to large parallax non-rigid transformatio, a model based on image matching is proposed in this paper for splicing tasks. The model which combines the similarity of the global structure and the local structure can not only divide the feature point on the structural level but also can pair with each other. At the same time, the framework can be trained end-to-end. The training samples composed of simulation data sets are adopted to train the designed model which is tested and verifyed by the test sample sets composed of three kinds of mixed data. Compared with PCA-GM, the model proposed in this paper adapts well to splicing tasks. The matching accuracy can achieve 93.1% and 91.9% on the simulation data set and the small parallax splicing data respectively, which can initially solve the image matching problem in the traditional image splicing scene. However, a further research aiming at the problem of the matching accuracy on the large parallax data set can only achieve 74.5% will be needed.
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