Independent 3D Motion Detection through Robust Regression in Depth Layers

This paper presents a methodology for the detection of objects that move independently of the observer in a 3D dynamic environment. Independent 3D motion detection is formulated as a problem of robust regression applied to visual input acquired by a binocular, rigidly moving observer. The qualitative analysis of images acquired by a parallel stereo configuration yields a segmentation of a scene into depth layers. A depth layer consists of points of the 3D space with almost constant depth from the observer. Robust regression in the form of Least Median of Squares estimation is applied within each depth layer in order to segment the latter into coherently moving regions. Finally, a combination stage is applied across all layers in order to come up with an integrated view of independent motion in the whole 3D scene. In contrast to other existing approaches for independent motion detection which are based on the ill-posed problem of optical flow computation, the proposed methodology relies on normal flow fields for both stereo and motion processing. Experimental results show the effectiveness and robustness of the proposed scheme, which is capable of discriminating independent 3D motion in scenes with large depth variations.