基于Kalman滤波器的二维运动目标跟踪

Object Tracking of 2D Image Motion Based on Kalman Filtering

利用kalman滤波器结合递归最小二乘法(RLS)建立了一个基于模型的鲁棒跟踪器,该模型能够有效分割图像域内的目标,提取目标特征并在给定区域内实现连续跟踪。采用动态kalman滤波器自适应的更新目标模型的特征,实时的增加新的、稳定的图像特征,同时减少无效或影响较小的图像特征,随后由RLS来完成对既定特征目标的匹配搜寻。通过在FIRA Mirosot集控式足球机器人平台上的应用,该方法能够在规定区域内,有效的跟踪小球,且鲁棒性较强。

A novel tracking strategy which can robustly track an object within a fixed environment was proposed. A robust model--based tracker was defined using kalman filtering combined with recursive least squares （RLS）. The tracking was done by fitting successively more elaborate models on the tracked region, and the segmentation was done by extracting the regions of the image that are consistent with the computed model of the target. A competitive and efficient dynamic kalman filter were adopted to adaptively update the object model by adding new stable features as well as deleting inactive features. Then the matching search of the feature object was achieved by RLS. The approach was tested on FIRA Mirosot and tested in the context of ball tracking in the FIRA domain. The results show that the proposed approach can obtain excellent ability of tracking and robustness.