Adaptive Bootstrapping Management by Keypoint Clustering for Background Initialization

The availability of a background model that describes the scene is a prerequisite for many computer vision applications. In several situations, the model cannot be easily generated when the background contains some foreground objects (i.e., bootstrapping problem). In this letter, an Adaptive Bootstrapping Management (ABM) method, based on keypoint clustering, is proposed to model the background on video sequences acquired by mobile and static cameras. First, keypoints are detected on each frame by the A-KAZE feature extractor, then Density-Based Spatial Clustering of Application with Noise (DBSCAN) is used to find keypoint clusters. These clusters represent the candidate regions of foreground elements inside the scene. The ABM method manages the scene changes generated by foreground elements, both in the background model initialization, managing the bootstrapping problem, and in the background model updating. Moreover, it achieves good results with both mobile and static cameras and it requires a small number of frames to initialize the background model.