Background clutter produces a difficult problem for edge matching within model-based object tracking approaches. The solution of matching all possible candidate image features with the model features is computationally infeasible for real-time tracking. It is proposed to draw probabilistic samples of candidate sets based on measures for local topological constraints. Line features are constraint by parallel and junction constraints. Continuous measures are used for evaluation of the match of the features sets to avoid thresholds. This approach limits the number of matchings and processing time increases linearly with the number of features. Experiments show the correct selection among multiple candidates for different scenarios.