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 algorithm based on locally model matching of geometric active contour(LM-GAC) is proposed. 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

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. 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. 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.