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.