关键词: 视觉显著性, BRISK特征, 多特征融合, 一致性匹配聚类

Abstract: Traditionally, most tracking algorithms use the single feature to describe the object. In view of the insufficiency of traditional tracking algorithms in complicated background, this paper puts forward an object tracking algorithm used by multi-feature fusion and visual saliency. Firstly, this paper adopts a visual saliency mechanism for      manipulating color histogram data to get saliency feature, uses a hybridstrategy that fuses the BRISK (binary robust   invariant scalable keypoints) feature and saliency information to describe the image, and then extracts the object-     foreground model and object-background model. Moreover, the dynamic feature is extracted by the bidirectional optical flow and error metric and is fused with the static feature which is extracted by the adaptive searching mechanism. Finally, based on the data of matching and tracking procedure in the previous frame, this paper evaluates the object’s scale, rotation and center, and obtains the new target location in the current frame. Experiments show that the proposed algorithm can handle the tracking in the complicated background, and adapt to strong illumination changes, partial occasions, fast motion and so on. The high accuracy and robustness of the proposed algorithm are confirmed by compared with the related algorithms, and the effectiveness and real-time of the method is validated.

Key words: visual saliency, BRISK feature, multi-feature fusion, consensus-based matching and clustering