Robust Non-rigid Object Tracking Using Point Distribution Manifolds
We present an approach to non-rigid object tracking designed to handle textured objects in crowded scenes captured by non-static cameras. For this purpose, groups of low-level features are combined into a model describing both the shape and the appearance of the object. This results in remarkable robustness to severe partial occlusions, since overlapping objects are unlikely to be indistinguishable in appearance, configuration and velocity all at the same time. The model is learnt incrementally and adapts to varying illumination conditions and target shape and appearance, and is thus applicable to any kind of object. Results on real-world sequences demonstrate the performance of the proposed tracker. The algorithm is implemented with the aim of achieving near real-time performance.
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