  Given a set of detections, detected at each time instant independently, we
investigate how to associate them across time. This is done by propagating
labels on a set of graphs, each graph capturing how either the spatio-temporal
or the appearance cues promote the assignment of identical or distinct labels
to a pair of detections. The graph construction is motivated by a locally
linear embedding of the detection features. Interestingly, the neighborhood of
a node in appearance graph is defined to include all the nodes for which the
appearance feature is available (even if they are temporally distant). This
gives our framework the uncommon ability to exploit the appearance features
that are available only sporadically. Once the graphs have been defined,
multi-object tracking is formulated as the problem of finding a label
assignment that is consistent with the constraints captured each graph, which
results into a difference of convex (DC) program. We propose to decompose the
global objective function into node-wise sub-problems. This not only allows a
computationally efficient solution, but also supports an incremental and
scalable construction of the graph, thereby making the framework applicable to
large graphs and practical tracking scenarios. Moreover, it opens the
possibility of parallel implementation.
