Fast Visual Detection of Changes in 3D Motion

A method is proposed for the fast detection of objects that maneuver in the visual field of a monocular observer. Such cases are common in natural environments where the 3D motion parameters of certain objects (e.g. animals) change considerably over time. The approach taken conforms with the theory of purposive vision, according to which vision algorithms should solve many, specific problems under loose assumptions. The method can effectively answer two important questions: (a) whether the observer has changed his 3D motion parameters, and (b) in case that the observer has constant 3D motion, whether there are any maneuvering objects (objects with non-constant 3D motion parameters) in his visual field. Essentially, the method relies on a pointwise comparison of two normal flow fields which can be robustly computed from three successive frames. Thus, it by-passes the ill-posed problem of optical flow computation. Experimental results demonstrate the effective~less and robustness of the proposed scheme. Moreover, the computational requirements of the method are extremely low, making it a likely candidate for real-time implementation.