Using middle level features for robust shape tracking

Shape tracking with low level features (e.g., edge points) often fails in complex environments (e.g., in the presence of clutter, inner edges, or multiple objects). Two alternative methods are discussed in this paper. Both methods use middle level features (data centroids, strokes) which are more informative and reliable than edge transitions used in most tracking algorithms. Furthermore, it is assumed in this paper that each feature can be either a valid measurement or an outlier. A confidence degree is assigned to each feature or to a given interpretation of all visual features. Features/ interpretations with high degrees of confidence have large influence on the shape estimates while features/interpretations with low degrees of confidence have negligible influence on the shape estimates. It is shown in this paper that both items (middle level features and confidence degrees) lead to a significant improvement of the tracker robustness and performance in the presence of clutter and abrupt shape and motion changes.