People Tracking and Segmentation Using Efficient Shape Sequences Matching

We design an effective shape prior embedded human silhouettes extraction algorithm. Human silhouette extraction is found challenging because of articulated structures, pose variations, and background clutters. Many segmentation algorithms, including the Min-Cut algorithm, meet difficulties in human silhouette extraction. We aim at improving the performance of the Min-Cut algorithm by embedding shape prior knowledge. Unfortunately, seeking shape priors automatically is not trivial especially for human silhouettes. In this work, we present a shape sequence matching method that searches for the best path in spatial-temporal domain. The path contains shape priors of human silhouettes that can improve the segmentation. Matching shape sequences in spatial-temporal domain is advantageous over finding shape priors by matching shape templates with a single likelihood frame because errors can be avoided by searching for the global optimization in the domain. However, the matching in spatial-temporal domain is computationally intensive, which makes many shape matching methods impractical. We propose a novel shape matching approach that has low computational complexity independent of the number of shape templates. In addition, we investigate on how to make use of shape priors in a more adequate way. Embedding shape priors into the Min-Cut algorithm based on distances from shape templates is lacking because Euclidean distances cannot represent shape knowledge in a fully appropriate way. We embed distance and orientation information of shape priors simultaneously into the MinCut algorithm. Experimental results demonstrate that our algorithm is efficient and practical. Compared with previous works, our silhouettes extraction system produces better segmentation results.