An Image Match Method Based on Optical Flow Feature Clustering for Vehicle-borne Panoramic Image Sequence

doi:10.13485/j.cnki.11-2089.2014.0172

Abstract

Abstract:

An image match method based on optical flow feature clustering is presented for vehicle-borne panoramic image sequence. The spatial domain and range domain of image feature space are built by the coordinates of SIFT multi-scale feature matching point and optical flow vector, then the panoramic image match is finished by Mean Shift attached to the optical flow clustering constraint condition in image feature space. Finally, panoramic geometric constraint of Ransac method is used for gross error detection. Several panoramic images are selected and used for experiment. The experiments of analysis and comparison were carried out in the conditions of the same inlier ratio, different inlier ratio and different data. The results show that the proposed method in the number and accuracy of correct matching points are superior to classic Ransac method and Pyramid Lucas-Kanade method, especially in the complex scene in low inlier ratio cases, the algorithm performance is relatively stable, and have better constraint effect for the gross error usually caused by repeat texture, moving objects and scale change.

Key words: vehicle-borne panoramic image, optical flow clustering, mean shift, SIFT feature

CLC Number:

P237

ZHANG Zhengpeng, JIANG Wanshou, ZHANG Jing. An Image Match Method Based on Optical Flow Feature Clustering for Vehicle-borne Panoramic Image Sequence[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(12): 1266-1273.

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