关键词: 特征匹配, SIFT特征, 目标检测, 目标定位

Abstract:

In object detection and localization system based on local feature matching,the number of match and false match effects directly on localization accuracy. An improved SIFT matching algorithm was proposed to decrease false match and meanwhile kept the sufficient correct match number. After analyzing different match result with different match threshold in conventional SIFT feature match method, an iterative strategy for adaptive dual-threshold for image match was presented. Then a geometry constrained model based on sparse but accurate match achieved with high threshold was establish to eliminate false match in dense match set achieved with low threshold. Experimental results show that compared with other methods, the proposed method has higher match accuracy which improves the performance of object detection and localization.

Key words: feature match, scale invariant feature transform(SIFT) feature, object detection, object localization