Motion detection with false discovery rate control

Visual surveillance applications such as object identification, object tracking, and anomaly detection require reliable motion detection as an initial processing step. Such a detection is often accomplished by means of background subtraction which can be as simple as thresholding of intensity difference between movement-free background and current frame. However, more effective background subtraction methods employ probabilistic modeling of the background followed by probability thresholding. In this case, the balance between false positives and false negatives (misses) is controlled by a threshold that needs to be adjusted heuristically depending on object sparsity. In this paper, we propose a different detection method that is based on false discovery rate control, a multiple-comparison procedure that applies thresholding in significance-score rather than probability space. The proposed approach allows explicit control of false positives and automatically adapts to object sparsity. The new method offers a qualitative improvement in real scenarios as well as a measurable performance gain over non-adaptive techniques when tested on synthetic sequences.