Object tracking based on the modular active shape model

The active contour model (ACM) was proposed by Kass et al. in 1988 (M. Kass, A. Witkin, D. Terzopoulos, Snake: Active contour models, Int. J. Comput. Vision (1988) 321-331) for segmentation and tracking of target objects in image space. In their theory some kinds of energies were designed to extract the boundaries of targets by giving higher or lower values of snake energy to them. This concept has been revised and developed by a few engineers in the field of computer vision to make their active contour models have a prior shape information in their energy equations, more robust characteristics, adaptive abilities in the elastic parameters, and so on. In spite of these efforts on ACMs, some basic problems such as weakness to strong surrounding edges, the drift of snaxels due to the changes of illumination conditions and sensitivity to cluttered environments, are still substantial problems in the applications of ACM to real environments. This paper proposes the design and an implementation of a tracking scheme with a combination of the ACM and the active shape model (ASM), in which the point distribution models are systematically constructed on the projective point of view. In this paper there are three main contributions. Firstly, the combination of the ACM and the ASM is tested to implement a model-based visual tracking system. Secondly, a systematical approach is proposed to construct a few individual point distribution models (PDM) generated on the basis of the projection relation. Finally, the modular active shape model (MASM) is designed to integrate the results of the principal component analysis (PCA) on the individually generated PDMs. By this concept the independent ideas by designers on the shape variations of the target are naturally integrated into the MASM which is modularly constructed on the basis of individual PDMs. As a result, a model-based visual tracker, the MASM, is designed to overcome the problems of the ACM, while the ideas of designer are systematically integrated in this MASM to include the expected variations on the target.