Distributed curve matching in camera networks using projective joint invariant signatures

An efficient method based on projective joint invariant signatures is presented for distributed matching of curves in a camera network. The fundamental projective joint invariants for curves in the real projective space are the volume cross-ratios. A curve in m-dimensional projective space is represented by a signature manifold comprising n-point projective joint invariants, where n is at least m + 2. The signature manifold can be used to establish equivalence of two curves in projective space. However, without correspondence between the two curves, matching signature manifolds is a computational challenge. In this paper we overcome this challenge by finding discriminative sections of signature manifolds consistently across varying viewpoints and scoring the similarity between these sections. This motivates a simple yet powerful method for distributed curve matching in a camera network. Experimental results with real data demonstrate the classification performance of the proposed algorithm with respect to the size of the sections of the invariant signature in various noisy conditions.