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Image processing is widely considered an essential part of future driver assistance systems. This paper presents a motion-based vision approach to initial detection of static and moving objects observed by a monocular camera attached to a moving observer. The underlying principle is based on parallax flow induced by all non-planar static or moving object of a 3D scene that is determined from optical flow measurements. Initial object hypotheses are created in regions containing significant parallax flow. The significance is determined from planar parallax decomposition automatically. Furthermore, we propose a separation of detected image motion into three hypotheses classes, namely coplanar, static and moving regions. To achieve a high degree of robustness and accuracy in real traffic situations some key processing steps are supported by the data of inertial sensors rigidly attached to our vehicle. The proposed method serves as a visual short-range surveillance module providing instantaneous object candidates to a driver assistance system. Our experiments and simulations confirm the feasibility and robustness of the detection method even in complex urban environment.
Baehring et al. (Sat,) studied this question.