A unifying software architecture for model-based visual tracking

In this paper we propose a general, object-oriented software architecture for model-based visual tracking. The library is general purpose with respect to object model, estimated pose parameters, visual modalities employed, number of cameras and objects, and tracking methodology. The base class structure provides the necessary building blocks for implementing a wide variety of both known and novel tracking systems, integrating different visual modalities, like as color, motion, edge maps etc., in a multi-level fashion, ranging from pixel-level segmentation, up to local features matching and maximum-likelihood object pose estimation. The proposed structure allows integrating known data association algorithms for simultaneous, multiple object tracking tasks, as well as data fusion techniques for robust, multi-sensor tracking; within these contexts, parallelization of each tracking algorithm can as well be easily accomplished. Application of the proposed architecture is demonstrated through the definition and practical implementation of several tasks, all specified in terms of a self-contained description language.