基于局部模型匹配的几何活动轮廓跟踪

Geometric active contour tracking based on locally model matching

目的在复杂背景下,传统轮廓跟踪方法只考虑了目标的整体特征或显著性特征,没有充分利用目标的局部特征信息,尤其是目标发生遮挡时,容易发生跟踪漂移,甚至丢失目标。针对上述问题,提出一种基于局部模型匹配的几何活动轮廓(LM-GAC)跟踪算法。方法首先,利用超像素技术将图像中的颜色特征相似的像素点归为一类,形成由一些像素点组成的超像素,从而把目标分割成若干个超像素块,再结合EMD(earth mover's distance)相似性度量构建局部特征模型。然后,进行局部模型匹配,引入噪声模型来估算局部模型参数θ,这样可以增强特征模型的自适应性,提高局部模型匹配的准确性。最后,结合粒子滤波的水平集分割方法提取目标轮廓,实现目标轮廓精确跟踪。结果本文算法与多种目标轮廓跟踪算法进行对比,在部分遮挡、目标形变、光照变化、复杂背景等条件的基准图像序列均具有较高的跟踪成功率,平均成功率为79.6%。结论实验结果表明,根据不同的图像序列,可以自适应地实时改变噪声模型参数和粒子的权重,使得本文算法具有较高的准确性和鲁棒性。特别是在复杂的背景下,算法能较准确地进行目标轮廓跟踪。

Objective Majority of traditional contour tracking methods only consider the overall characteristics or significant features of the moving target under a complex background, which figure out contour tracking without fully utilizing the moving target＇s locally feature information. When the moving target is occluded, most traditional tracking methods make these moving target easily drift, which sometimes result in the loss of the moving target. Focusing on these problems, tracking al- gorithm based on locally model matching of geometric active contour （LM-GAC） is proposed. Method Super-pixels make these similar color characteristics of pixels in the image as a class ; thus, a plurality of pixels is composed of super-pixels. Super-pixels divide the moving target into a plurality of pixel blocks. The super-pixel is combined with the EMD （earth mover＇s distance） similarity measure to build locally feature model. Carrying on locally model matching, a noise model is then introduced to estimate the local model parameter θ, which can enhance the adaptiveness of the features model and the accuracy of the locally model matching. Finally, the level set segmentation method is combined with particle filter to extract the moving target contours to track moving target contours accurately. Result Compared with other moving target contour tracking methods, the proposed moving target tracking method maintains a higher success rate on image sequences that were under the conditions of partial occlusion, target deformation, illumination changes, and complex background. The proposed moving target tracking method, which has an average Success rate reaching 79. 6% , is relatively accurate and stable. Conclusion Experiment results indicate that the proposed moving target tracking algorithm can modify noise model parameters and particles heavy adaptively in real timedepending on the image sequence, so the proposed moving target tracking algorithm has higher accuracy and robustness. Under complex backgrounds, the proposed moving target tracking algorithm can track the moving target contour more accurately.