+The two main classes are MTPGraph and MTPTracker.
+
+The MTPGraph class contains a directed acyclic graph (DAG), with a
+length for each edge -- which can be negative -- and has methods to
+compute the family of paths in this graph that globally minimizes the
+sum of edge lengths.
+
+If there are no path of negative length, this optimal family will be
+empty, since the minimum total length you can achieve is zero. Note
+that the procedure is similar to that of KSP, in the sense that the
+family it computes eventually is globally optimal, even if the
+computation is iterative.
+
+The MTPTracker takes as input
+
+ (1) a number of locations and a number of time steps
+
+ (2) a spatial topology composed of
+
+ - the allowed motions between them (a Boolean flag for each pair
+ of locations from/to)
+
+ - the entrances (a Boolean flag for each location and time step)
+
+ - the exits (a Boolean flag for each location and time step)
+
+ (3) a detection score for every location and time, which stands for
+
+ log( P(Y(l,t) = 1 | X) / P(Y(l,t) = 0 | X) )
+
+ where Y is the occupancy of location l at time t and X is the
+ available observation. Hence, this score is negative on locations
+ where the probability that the location is occupied is close to
+ 0, and positive when it is close to 1.
+
+From this parameters, the MTPTracker can compute the best set of
+disjoint trajectories consistent with the defined topology, which
+maximizes the overall detection score (i.e. the sum of the detection
+scores of the nodes visited by the trajectories). In particular, if no
+trajectory of total positive detection score exists, this optimal set
+of trajectories is empty.