  One of the fundamental requirements for visual surveillance using smart
camera networks is the correct association of each persons observations
generated on different cameras. Recently, distributed data association that
involves only local information processing on each camera node and mutual
information exchanging between neighboring cameras has attracted many research
interests due to its superiority in large scale applications. In this paper, we
formulate the problem of data association in smart camera networks as an
Integer Programming problem by introducing a set of linking variables, and
propose two distributed algorithms, namely L-DD and Q-DD, to solve the Integer
Programming problem using dual decomposition technique. In our algorithms, the
original IP problem is decomposed into several sub-problems, which can be
solved locally and efficiently on each smart camera, and then different
sub-problems reach consensus on their solutions in a rigorous way by adjusting
their parameters based on projected sub-gradient optimization. The proposed
methods are simple and flexible, in that (i) we can incorporate any feature
extraction and matching technique into our framework to measure the similarity
between two observations, which is used to define the cost of each link, and
(ii) we can decompose the original problem in any way as long as the resulting
sub-problem can be solved independently on individual camera. We show the
competitiveness of our methods in both accuracy and speed by theoretical
analysis and experimental comparison with state of the art algorithms on two
real data sets collected by camera networks in our campus garden and office
building.
