[Abstract] [Experimental Results] [References] [PDF]
With the extensive potential applications of computer technologies, automatic object segmentation plays a more and more important role in digital video processing, pattern recognition, and computer vision. In this paper, we propose an automatic human body segmentation system mainly consisting of human body detection and object segmentation. Firstly, an automatic human body detector is designed to provide hard constraints on the object and background for future segmentation. Secondly, in the first frame to be segmented, a coarse-to-fine segmentation strategy is employed to deal with the situation of partly detected object. By investigating the well-known graph cut segmentation algorithm and its implementations, we find that the segmentation error often occurs at the object boundary with the clattered background which will result in unpleasant visual artifacts in the segmented video. Therefore, background contrast removal (BCR) is proposed to weaken the high contrast in the background and preserve the contrast belonging to the foreground and background simultaneously. Thirdly, we propose a self-adaptive initialization level set (SAILS) to solve the tough problem, the undefined boundary in region-based segmentation, and to speed up the process of evolution simultaneously. Finally, an object updating scheme is proposed to detect and re-initialize new object when object disappears and reappears in the scene. Experimental results demonstrate that our body segmentation system works very well in the live video with strong edge and similar color in the background.
Experimental Results (Compressed by JPEG)
Fig.1 Comparison with "Background cut" : the
input images are listed in the first row, the second and last row are the
segmented results by "Background cut" and our method.
the improvement is marked by white ellipses.
Fig.2 Comparison with Level Set : the red curves are
the segmented boundary of object and background. the first row is the segmented
results by Level Set
and the second row is our method's results. the improvement is marked by white ellipses.
Fig.3 Segmentation results of "In lab" and "ZW" sequence with partly moving background and similar color in the background: the input images are listed in the odd rows and the corresponding results are shown in the even rows.
Fig.4 Segmentation results of "In office" sequence with head and body moving: the input images are listed in the odd rows and the corresponding results are shown in the even rows.
Fig.5 Segmentation results of outdoor sequence with moving people: the input images are listed in the odd rows and the corresponding results are shown in the even rows.
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