Semantic Multi-body Motion Segmentation
This paper presents a method to deal with the multi-body segmentation problem using a set of 2D points matches between two views. The key feature of our approach is the explicit inclusion of a higher semantic information as given by general purpose object detectors that boost the segmentation of the moving objects. In the classical formulation of the problem, only 2D matched points between views are used to identify independently moving objects based on the principle that a set of points belonging to a moving object would satisfy some given multi-view relations (e.g. multi-body epipolar constraints). We improve and speedup such process by including the information that a set of 2D matches may belong to the same object given the output of a detector. As such, instead of sampling points uniformly with a RANSAC based strategy, the selection of the matches is driven by the position and score confidence of the object detectors. Evaluation on challenging synthetic and real datasets shows a remarkable improvement in respect to previous approaches, regarding both the number of iterations required to segment a scene and the effectiveness of the segmentation itself, often making the difference between satisfying segmentation and almost complete failure.