Smart tracking systems are nowadays a necessity in different fields, especially the industrial one. A very interesting and successful open source software has been developed by the University of Padua, called OpenPTrack. The software, based on ROS (Robotic Operative System), is capable to keep track of humans in the scene, leveraging well known tracking algorithms that use point cloud 3D information, and also objects, leveraging the colour information as well.
Amazed by the capabilites of the software, we decided to study its performances further. This is the aim of this thesis project: to carefully characterize the measurment performances of OpenPTrack both of humans and objects, by using a set of Kinect v2 sensor.
Step 1: Calibration of the sensors
It is of utmost importance to correctly calibrate the sensors when performing a multi-sensor acquisition.
Two types of calibration are necessary: (i) the intrinsic calibration, to align the colour (or grayscale/IR like in the case of OpenPTrack) information acquired to the depth information (Fig. 1) and (ii) the extrinsic calibration, to align the different views obtained by the different cameras to a common reference system (Fig. 2).
The software provides the suitable tools to perform these steps, and also provides a tool to further refine the extrinsic calibration obtained (Fig. 3). In this case, a human operator has to walk around the scene: its trajectory is then acquired by every sensor and at the end of this registration the procedure aligns the trajectories in a more precise way.
Each of these calibration processes is completely automatic and performed by the software.