Name
Abstract | ||
|---|---|---|
To improve the localization accuracy and robustness of the moving 3D-target under the nature scenes, we propose a new target localization method through combining MSER (Maximally Stable Extremal Region) detector with SIFT (Scale Invariant Feature Transform) descriptor into the dual-PTZ-cameras stereo vision system. Firstly, stereo vision rectification is performed on the right-and-left images captured from the dual-PTZ-cameras with different focal lengths using designed Look-up-table(LUT )and BP neural network. Secondly, more high quality affine invariant features are extracted from the rectified images to perform initial matching using affine invariant feature detector and descriptor. Thirdly, erroneous correspondences is detected by RANSAC. Then, robust features matching under the multi-view-point and multi-focal-length is achieved. The localization experimental results of the moving 3-D target in a complex environment show that the proposed method has good localization accuracy and robustness. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33503-7_45 | ICIRA (3) |
Keywords | Field | DocType |
stereo localization,3-d target,dual-ptz-cameras stereo vision system,localization accuracy,localization experimental result,good localization accuracy,high quality affine invariant,new method,new target localization method,initial matching,affine invariant feature detector | Computer vision,Scale-invariant feature transform,Lookup table,Pattern recognition,Stereopsis,RANSAC,Robustness (computer science),Principal curvature-based region detector,Artificial intelligence,Artificial neural network,Detector,Mathematics | Conference |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
5 | ||