2011.
This implementation is not the reference implementation used for the
-experiments presented in this article.
+experiments presented in this article. It uses a Dijkstra with a
+Binary Heap for the min-queue, and not the optimal Fibonacci heap.
* INSTALLATION
-This software should compile with any C++ compiler. Just execute
+This software should compile with any C++ compiler. Under a unix-like
+environment, just execute
make
./mtp_example
./mtp tracker.dat
-It will load the tracker.dat saved by the previous command, run the
-detection, save the detected trajectories in result.trj, and the
+It will load the file tracker.dat saved by the previous command, run
+the detection, save the detected trajectories in result.trj, and the
underlying graph with occupied edges in graph.dot.
-You can produce a pdf from the latter with the dot command from
-graphviz:
+If you do have the graphviz set of tools installed, you can produce a
+pdf from the latter with the dot command:
dot < graph.dot -T pdf -o graph.pdf
The two main classes are MTPGraph and MTPTracker.
-The MTPGraph class stores a directed acyclic graph (DAG), with a
-length for each edge -- which can be negative -- and can compute the
-family of paths in this graph that globally minimizes the sum of edge
-lengths.
+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.
-This means that it will iteratively add paths as long as it can find
-some with negative length. If there are no such path, it will compute
-no path at all. 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.
+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 is defined by
(2) a number of time steps
- (3) for every location and time a detection score, which should stand
- for log(P(Y = 1 | X)/P(Y = 0 | X)) where Y is for the location
- occupancy and X the available observations.
+ (3) a detection score for every location and time, which stands for
-From this setting, it computes the best set of disjoint trajectories
-consistent with the topology, which maximizes the overall detection
-score (i.e. the sum of the detection scores of the nodes visited by
-the trajectories)
+ log( P(Y(l,t) = 1 | X) / P(Y(l,t) = 0 | X) )
-The MTPTracker is a wrapper around the MTPGraph class.
+ where Y is the occupancy of location l at time t and X is the
+ available observation.
-From the defined spatial topology and number of time steps, it builds
-a graph with one source, one sink, and two nodes per location and
-time. This structure ensures that the trajectories computed by the
-MTPTracker will be node-disjoint, since the trajectories computed by
-the MTPGraph are edge-disjoint.
+From this setting, MTPTracker has methods to 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). If no trajectory of
+total positive detection score exists, this optimal set of
+trajectories will be empty.
-The edges from the source or to the sink, or between these pairs of
-nodes, are of length zero, and the edges between the two nodes of such
-a pair have negative lengths, equal to the opposite of the
-corresponding detection scores.
+The MTPTracker is a wrapper around the MTPGraph class. From the
+defined spatial topology and number of time steps, it builds a graph
+with one source, one sink, and two nodes per location and time. The
+edges from the source or to the sink, or between these pairs of nodes,
+are of length zero, and the edges between the two nodes of such a pair
+have negative lengths, equal to the opposite of the corresponding
+detection scores. This structure also ensures that the trajectories
+computed by the MTPTracker will be node-disjoint, since the
+trajectories computed by the MTPGraph are edge-disjoint.
The file mtp_example.cc gives a very simple usage example of the
MTPTracker class by setting the tracker parameters dynamically, and
The tracker data file for MTPTracker::read has the following format,
where L is the number of locations and T is the number of time steps:
- ---------------------------- snip snip -------------------------------
+---------------------------- snip snip -------------------------------
int:L int:T
bool:allowed_motion_from_1_to_1 ... bool:allowed_motion_from_1_to_L
float:detection_score_1_1 ... float:detection_score_1_L
...
float:detection_score_T_1 ... float:detection_score_T_L
- ---------------------------- snip snip -------------------------------
+---------------------------- snip snip -------------------------------
The method MTPTracker::write_trajectories writes first the number of
trajectories, followed by one line per trajectory with the following
structure
- ---------------------------- snip snip -------------------------------
+---------------------------- snip snip -------------------------------
int:traj_number int:entrance_time int:duration float:score int:location_1 ... int:location_duration
- ---------------------------- snip snip -------------------------------
+---------------------------- snip snip -------------------------------
--
François Fleuret
-September 2012
+December 2012
projects / mtp.git / blobdiff