Comparison of static background segmentation methods

In the case of a static or motion compensated camera, static background segmentation methods can be applied to segment the interesting foreground objects from the background. Although a lot of methods have been proposed, a general assessment of the state of the art is not available. An important issue is to compare various state of the art methods in terms of quality (accuracy) and computational complexity (time and memory consumption). A representative set of recent techniques is chosen, implemented and compared to each other. An extensive set of videos is used to achieve comprehensive results. Both indoor and outdoor videos with different environmental conditions are used. While visual analysis is used for subjective assessment of the quality, pixel based measures based on available ground truth data are used for the objective assessment. Furthermore the computational complexity is estimated by measuring the elapsed time and memory requirements of each algorithm. The paper summarizes the experiments and considers the assets and drawbacks of the various techniques. Moreover, it will give hints for selecting the optimal approach for a specific environment and directions for further research in this field.