README.md

Relational Learner

A ROS package that uses qualitative spatio-temporal relations to learn and classify human trajectories as statistically novel or not.

Prerequisites

• roscore
• mongodb_store
• strands_perception_people
• soma_trajectory
• qsrlib

Getting started

1. Run mongodb datacentres:

   $ roslaunch mongodb_store mongodb_store.launch db_path:= <path>
   $ roslaunch mongodb_store mongodb_store.launch db_path:=/home/strands/mongodb_store/bham_trajectory_store/
   

where path specifies the path to your mongodb_store

Also run the mongodb robot pose logging tool: $ rosrun mongodb_log mongodb_log.py /robot_pose

2. Make sure people perception is running to publish detected trajectories:

   $ roslaunch perception_people_launch people_tracker_robot.launch
   

3. Also run the online-trajectory stiching tool:

   $ rosrun human_trajectory trajectory_publisher.py [publish_interval][online(1)/offline(0)]
   

see here for more details.

Alternatively to step 2 and 3, you can run soma_trajectory and obtain test trajectories from mongodb store: $ rosrun soma_trajectory trajectory_query_service.py

4. Make sure the QSRLib Service is running:

   $ rosrun qsr_lib qsrlib_ros_server.py 
   

5. Run the Episodes Service:

   $ rosrun relational_learner episode_server.py
   

6. Run the Novelty Service:

   $ rosrun relational_learner novelty_server.py
   

7. Run the Episodes node:

   $ rosrun relational_learner episodes_client.py
   

8. Run Novelty node:

   $ rosrun relational_learner novelty_client.py
   

Note:

This package can be run offline by running soma_trajectory in step 2 and 3 instead of people_perception. In this case, step 7 becomes: $ rosrun relational_learner episodes_client_OT.py

which queries one region's trajectories from mongodb_store instead of subscribing to published trajectories.