Recognizing Objects From Curvilinear Motion

The premise of this work is twofold: i) that an object can be recognized on the basis of the optical flow it induces on a stationary observer, and ii) that a basis for recognition can be built on the appearance of flow corresponding to local curvilinear motion. Unlike other approaches that seek to recognize particular motions, ours focuses on the problem of recognizing objects by training on an expected set of motions. A sequential estimation framework is used to solve the implicit factorization problem, which is itself simplified in that the task is to discriminate between different objects as opposed to recovering motion or structure. Training is accomplished automatically using a robot mounted camera system to induce a set of canonical motions at different locations on a viewsphere. Experimental results are presented to support our contention that the resulting motion basis can generalize to a fairly wide range of motions, leading to a practical method for recognizing moving objects.

Figure: As each flow image in the sequence is introduced to the system, probabilistic evidence is cascaded until a clear winner emerges. The idea is that strong prior evidence should resolve ambiguities and lead to a winning solution in a short number of views.

A presentation  on this topic was given at the Eleventh British Machine Vision Conference, Bristol, UK, 11-14 September 2000.