Pfinder explicitly employs several domain-specific assumptions to make the vision task tractable. When these assumptions break, the system degrades in specific ways. Due to the nature of Pfinder's structure and since the model of the user is fairly weak, the system degrades gracefully and recovers in two or three frames once the assumption again holds.
Pfinder expects the scene to be significantly less dynamic than the user. Although Pfinder has the ability to compensate for small, or gradual changes in the scene or the lighting, it cannot compensate for large, sudden changes in the scene. If such changes occur, they are likely to be mistakenly considered part of the foreground region, and an attempt will be made to explain them in the user model.
Another limitation, related to the dynamic scene problem, is that system expects only one user to be in the space. Multiple users don't cause problems in the low level segmentation or blob tracking algorithms, but do cause significant difficulties with the gesture recognition system that attempts to explain the blob model as a single human figure.