基于边缘信息的灰度目标跟踪算法

提出了一种基于边缘信息的跟踪算法,其可以实现对剧烈变化的灰度目标的精确跟踪.首先,利用基于双同心圆窗口算子的非线性边缘检测算法得到高质量的边缘信息;其次,为了解决单一边缘特征空间不能充分表征目标的难题,提出了一种通过组合边缘图像构建特征空间的方法,以便为目标建模提供充分信息;再次,在构建的特征空间中使用核估计方法对目标进行建模;在目标定位阶段,利用Kalman滤波器对目标进行预估后,再由Mean Shift算法在预估位置邻近区域实现目标定位;最后,提出了一种基于形态学的动态模型更新策略,使得算法不仅可以获得精确的目标区域,还可以实现对目标尺寸和形状变化的自适应.实验结果表明,本算法不仅可以有效跟踪剧烈变化的灰度目标,而且跟踪窗口可以实现对目标尺寸和形状的自适应.

Gray target tracking algorithm based on edge information

To precisely track the gray targets undergoing drastic changes in the image sequence, a new tracking algorithm based on edge information was proposed. Firstly, obtained by the two-concentric-circular-window operator, a nonlinear edge detection algorithm was proposed to get high quality edge information. Secondly, a novel method to construct feature space by synthesizing edge images was proposed in order to solve the problem that single edge feature space was not able to characterize the target thoroughly. The proposed method provided enough information to construct target model. Then, an approach to construct the target model with the kernel-based estimation method was proposed in constructed feature space. In target localization stage, the target position was preliminarily predicted by Kalman filter, and then the Mean Shift algorithm is utilized to locate the target in the region around the predicted position. Finally, a new dynamic model update strategy based on morphological operations was proposed. It can offer the proposed algorithm the ability to obtain precise target region and automatically adjust to the changing target size and target shape. Experimental results demonstrate that the proposed algorithm can perform well in image sequences where the targets undergo drastic changes. Meanwhile, the proposed algorithm can obtain the precise target region, and the track window can automatically adjust to the changing target size and target shape.